• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.9-16
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• 2023

Technological advancement is being advanced in sports such as electronic protection of taekwondo competition and VAR of soccer. However, a person judges and guides the posture by looking at the posture, so sometimes a judgment dispute occurs at the site of the competition in Taekwondo Poomsae. This study proposes an artificial intelligence model that can more accurately judge and evaluate Taekwondo movements using artificial intelligence. In this study, after pre-processing the photographed and collected data, it is separated into train, test, and validation sets. The separated data is trained by applying each model and conditions, and then compared to present the best-performing model. The models under each condition compared the values of loss, accuracy, learning time, and top-n error, and as a result, the performance of the model trained under the conditions using ResNet50 and Adam was found to be the best. It is expected that the model presented in this study can be utilized in various fields such as education sites and competitions.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.867-871
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• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.

• The Journal of Bigdata
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• v.7 no.1
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• pp.63-73
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• 2022

COVID-19 has continued from 2020 to the present, and many social changes have occurred. Wearing a mask has become mandatory, and if you do not wear a mask, you cannot use public facilities or restaurants. For this reason, most public facility entrances are equipped with a mask recognition system to check whether a mask is worn. However, it is unclear whether people who cover their mouths with a scarf or who do not wear a mask properly can be identified. In this study, we proposed an embedded mask recognition system using YOLOv5. Unlike the existing mask recognition system, it was able to distinguish not only whether a mask was worn, but also whether a mask was worn in various exceptional situations, such as a person with a scarf or a person covering their mouth with their hands, and showed excellent performance when mounted on the Nvida Jetson Nano Board.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.917-922
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• 2019

The recent increase in preference for teacher jobs has led to a rise in preference for education colleges. Not all students can enter teachers, but they must pass the test called the competitive examination for teacher appointment candidates after graduation. However, due to the declining population, the and employment T.O.s are decreasing every year and the competition rate is rising steeply. Therefore, in order to concentrate on the recruitment exam upon entering the university, the university is becoming a huge academy for the exam, not a place to study and learn. We found a connection between students' overall school life and their use of study groups as well as their grades and whether they passed the competition test for teachers using deep running. The academic activities did not significantly affect the acceptance process, and the accuracy of the prediction of the acceptance rate was generally 70% accurate.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.4
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• pp.129-149
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• 2022

Records have temporal characteristics, including the past and present; linguistic characteristics not limited to a specific language; and various types categorized in a complex way. Processing records such as text, video, and audio in the life cycle of records' creation, preservation, and utilization entails exhaustive effort and cost. Primary natural language processing (NLP) technologies, such as machine translation, document summarization, named-entity recognition, and image recognition, can be widely applied to electronic records and analog digitization. In particular, Korean deep learning-based NLP technologies effectively recognize various record types and generate record management metadata. This paper provides an overview of Korean NLP technologies and discusses considerations for applying NLP technology in records management. The process of using NLP technologies, such as machine translation and optical character recognition for digital conversion of records, is introduced as an example implemented in the Python environment. In contrast, a plan to improve environmental factors and record digitization guidelines for applying NLP technology in the records management field is proposed for utilizing NLP technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1790-1796
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• 2021

A large amount of image data sets are required for image deep learning, and there are many differences in the method of obtaining images and constructing image data sets depending on the type of object. In this paper, we presented a method of constructing an image data set for deep learning and analyzed the performance that varies depending on the object to be tracked. We took a video by rotating the object, and then created a data set by segmenting the video using the proposed data set construction method. As a result of performance analysis, detection rate was more than 95%, and detection rate of objects with little change in shape was higher performance. It is considered that it is effective to use the data set construction method presented in this paper for a situation in which it is difficult to obtain image data and to track an object with little change in shape within a video.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.513-520
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• 2021

Recently, research on edutech, which combines education and technology in the e-learning field called learning, education and training, has been actively conducted, but it is still insufficient to collect and utilize data tailored to individual learners based on learning activity data that can be automatically collected from digital devices. Therefore, this study attempts to detect questions in textbooks or problem papers using artificial intelligence computer vision technology that plays the same role as human eyes. The textbook or questionnaire item detection model proposed in this study can help collect, store, and analyze offline learning activity data in connection with intelligent education services without digital conversion of textbooks or questionnaires to help learners provide personalized learning services even in offline learning.

• Korean Educational Research Journal
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• v.40 no.2
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• pp.1-16
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• 2019

The society of intelligence-information complex is a fresh world that connects things, knowledge and calculation with human. What is the condition of educational reform in this world? Robinson and Aronica(2015) suggest educational reform at the center of organic agriculture, in which they focus on the dignity of human as an organic being. Human consists in an intelligence and a life. We have to ask to ourselves what is the human in this Age. The development of AI represented by deep-learning will be an actual condition in the educational reform. In the other hand, the combination with an information technology and art rises a question about a life itself. So, we have to ask the question seriously that overlap what is the human and what is a life. Two questions about human and a life cast a philosophical paradox in the age of AI.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.