• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.663-671
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• 2017

This study analyzed the determinants influencing competitiveness of artificial intelligence industry of Korea. Analysis showed that the factors having the greatest influence on the national competitiveness of the artificial intelligence industry were, in decreasing order of importance, R&D, capital, and ICT competitiveness. Given the capital-intensive characteristic of the artificial intelligence technology, it is important to enhance the national R&D capacity in artificial intelligence technology and ICT, and to make substantial investments in the establishment of related infrastructure. Considering that the development and utilization of ICT infrastructure serves as the basis for artificial intelligence technology, the high standards of Korean consumers are expected to have a positive catalytic effect on the acquisition of national competitiveness for the artificial intelligence industry. For companies to respond in a timely manner to the rapid dissemination and high impact of artificial intelligence technology, they must prepare for such advancements by improving their competencies.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.969-976
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• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.16-21
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• 2019

Small and medium-sized Korean companies are currently changing their industrial structure faster than in the past due to various environmental factors (such as securing competitiveness and developing excellent products). In particular, the importance of collecting and utilizing data produced in smart factory environments is increasing as diverse devices related to artificial intelligence are put into manufacturing sites. This paper proposes an artificial intelligence-based smart factory model to improve the process of products produced at the manufacturing site with the recent smart factory. The proposed model aims to ensure the increasingly competitive manufacturing environment and minimize production costs. The proposed model is managed by considering not only information on products produced at the site of smart factory based on artificial intelligence, but also labour force consumed in the production of products, working hours and operating plant machinery. In addition, data produced in the proposed model can be linked with similar companies and share information, enabling strategic cooperation between enterprises in manufacturing site operations.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.339-351
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• 2020

In response to changes in Fourth Industrial Revolution in recent years, the field of education has focused on development of the human resources in the areas of artificial intelligence (AI: Artificial Intelligence) and industrial robot. Due to particular interest in these areas, the importance of developing integrated human resources equipped with artificial intelligence technology is emphasized in higher education and vocational competence development. In regards to rapid changing environment, this study created a program "Fostering personalized AI integrated human resource" and established an operational model correspond to latest personalized education trend. The established operational model was conducted twice using Delphi survey with experts in AI and innovative education in order to verify the suitability of program's basic structure, training process, and the sub-components of the operational strategy. The final training model was applied to the online vocational training platform (STEP) and a plan was proposed to establish a personalized training model to foster an AI integrated competent individual.

• Journal of The Korean Association of Information Education
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• v.26 no.1
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• pp.65-73
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• 2022

In this study, by using Entry text model learning, educational contents for artificial intelligence education of elementary school students are developed and applied to actual classes. Based on the elementary and secondary artificial intelligence content table, the achievement standards of practical software education and artificial intelligence education will be reconstructed.. Among text, images, and sounds capable of machine learning, "production of emotion recognition programs using text model learning" will be selected as the educational content, which can be easily understood while reducing data preparation time for elementary school students. Entry artificial intelligence is selected as an education platform to develop artificial intelligence education contents that create emotion recognition programs using text model learning and apply them to actual elementary school classes. Based on the contents of this study, As a result of class application, students showed positive responses and interest in the entry AI class. it is suggested that quantitative research on the effectiveness of classes for elementary school students is necessary as a follow-up study.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.201-208
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• 2021

Recently, artificial intelligence(AI) has been used throughout society, and social interest in it is increasing. Accordingly, the necessity of AI education is becoming a big topic in the education field. As a response to this trend, the Korean education authorities have also announced plans for AI education, and various studies have been performed in academic field to revitalize AI education in the future. However, the curriculum research on what differentiates AI education from existing SW education and what and how to train AI is still in its infancy. In this paper, Therefore, we focused on the experiences of elementary school students in solving problems in their own lives, and developed a teaching-learning model based on design-based research so that students can design a problem-solving process and experience the process of feedback. We applied the developed teaching-learning model to the problem-solving process and confirmed that it increased students' understanding and satisfaction with AI education.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.58-65
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• 2023

Bankruptcy is a significant risk for start-up companies, but with the help of cutting-edge artificial intelligence technology, we can now predict bankruptcy with detailed explanations. In this paper, we implemented the Category Boosting algorithm following data cleaning and editing using OpenRefine. We further explained our model using the Shapash library, incorporating domain knowledge. By leveraging the 5C's credit domain knowledge, financial analysts in banks or investors can utilize the detailed results provided by our model to enhance their decision-making processes, even without extensive knowledge about AI. This empowers investors to identify potential bankruptcy risks in their business models, enabling them to make necessary improvements or reconsider their ventures before proceeding. As a result, our model serves as a "glass-box" model, allowing end-users to understand which specific financial indicators contribute to the prediction of bankruptcy. This transparency enhances trust and provides valuable insights for decision-makers in mitigating bankruptcy risks.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.11-19
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• 2024

This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector's level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.84-94
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• 2023

Open source has many advantages and is widely used in various fields. However, legal disputes regarding copyright and licensing of datasets and learning models have recently arisen in artificial intelligence developments. We examine how datasets affect artificial intelligence learning and services from the perspective of copyrighting and licensing when datasets are used for training models. The licensing conditions of datasets can lead to copyright infringement and license violation, thus determining the scope of disclosure and commercialization of the trained model. In addition, we examine related legal issues.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.87-95
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• 2018

Recently, with the technological development of artificial intelligence, related market is expanding rapidly. In the artificial intelligence technology field, which is still in the early stage but still expanding, it is important to reduce uncertainty about research direction and investment field. Therefore, this study examined technology trends using text mining and topic modeling among big data analysis methods and suggested trends of core technology and future growth potential. We hope that the results of this study will provide researchers with an understanding of artificial intelligence technology trends and new implications for future research directions.