• Journal of Digital Convergence
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• v.14 no.7
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• pp.167-174
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• 2016

The university and college turn into the field of job readiness to get a good job and students build a lot of job specification than others and are constantly studying for employment. Then since employment, some people are fortunate to keep the job for lifetime, but for many people work in the workplace did not meet his aptitude with patience and some people move for work several times without perseverance. One of the reasons for job dissatisfaction is that the job does not fit his aptitude. Meantime many organizations conducted the aptitude(Psychology) test. There are limits, however, to find a suitable job. This study was presented as a model of a platform that is a rational and scientific alternative to search course and job. This model is to better understand the individual characteristics using Big data and artificial intelligence, offers several jobs to meet the characteristics among the various professions selectively and supports to select and design an appropriate job based on the field experience, consulting and mentoring.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.74-85
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• 2019

This Study explores the reason why the social media like Cyworld, Iloveschool in Korea in the viewpoint if the layered model by interview. As a result the success factor in the viewpoint of layered model, user used social media for fulfilling the need for linking with other users and the social media offers the customized contents to user. Finally the social media dominated the market in advance. Facebook and Kakao talk are good examples of successful media. The failure factors are to care less about what other users want, to limit the expand of platform and not to copy with the change of the media environment. Iloveschool, Cyworld and Twitter are the examples of failure social media in Korean society. This study highlights the importance of the sensitivity of the change of environment. The expert mentioned the importance of 4th industrial revolution technology like AI, Big data and expected that new technology will emerge and the service will be developed by the change of user's taste.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.409-414
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• 2021

The growing concerns on the emission of particulate matter has prompted a demand for highly reliable particulate matter forecasting. Currently, several studies on particulate matter prediction use various deep learning algorithms. In this study, we compared the predictive performances of typical neural networks used for particulate matter prediction. We used deep neural network(DNN), recurrent neural network, and long short-term memory algorithms to design an optimal predictive model on the basis of a hyperparameter search. The results of a comparative analysis of the predictive performances of the models indicate that the variation trend of the actual and predicted values generally showed a good performance. In the analysis based on the root mean square error and accuracy, the DNN-based prediction model showed a higher reliability for prediction errors compared with the other prediction models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1571-1577
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• 2019

There is a growing interest in psychological counseling and pyschotherapy and many experts are working to help clients more efficiently. Given the many advantages of cyber counseling can be a good alternative to traditional face-to-face counseling. This paper examines the trends of cyber counseling and psychotherapy in South Korea. As a result, when comparing traditional counseling with cyber counseling, cyber counseling had advantages such as high accessibility, convenience, cost reduction effect, and inconvenience in face-to-face counseling. In addition, cyber counseling in South Korea began in the late 1980s, and many studies were conducted for teenagers who are familiar with ICT. Lastly, I briefly researched the current status of cyber counseling and psychotherapy in South Korea. Currently, more active cyber counseling and psychotherapeutic interventions are being carried out using images, AI, and VR. However, because the interdisciplinary approach is somewhat lacking, it is necessary to develop various counseling and psychotherapy programs and verify the effectiveness through the interdisciplinary approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2086-2097
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• 2021

The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.

• Informatization Policy
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• v.29 no.4
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• pp.43-66
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• 2022

With the development of artificial intelligence technology, competition for artificial intelligence technology patents around the world is intensifying. During the period 2000 ~ 2021, artificial intelligence technology patent applications at the US Patent and Trademark Office have been steadily increasing, and the growth rate has been steeper since the 2010s. As a result of analyzing Korea's artificial intelligence technology competitiveness through patent indices, it is evaluated that patent activity, impact, and marketability are superior in areas such as auditory intelligence and visual intelligence. However, compared to other countries, overall Korea's artificial intelligence technology patents are good in terms of activity and marketability, but somewhat inferior in technological impact. While noise canceling and voice recognition have recently decreased as topics for artificial intelligence, growth is expected in areas such as model learning optimization, smart sensors, and autonomous driving. In the case of Korea, efforts are required as there is a slight lack of patent applications in areas such as fraud detection/security and medical vision learning.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.349-354
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• 2021

In this study, a model that can converge with artificial intelligence in each subject as software and artificial intelligence education become mandatory in the curriculum revised in 2022 is proposed. The proposed AI convergence education model is based on the content of the subject (accomplishment standard + subject). The second axis is artificial intelligence tools, the third axis is artificial intelligence technology, and the fourth axis is data applied in daily life. In order to apply artificial intelligence to each subject, it is necessary to apply artificial intelligence tools, artificial intelligence technology, and data in daily life to the achievement standards and content of each subject. If the achievement standards and subject contents are structured in this way, it can be seen that the convergence with each subject is good. Therefore, when composing textbooks by achievement standards and topics, it is necessary to add artificial intelligence tools, artificial intelligence technology, and daily data.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.1-10
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• 2023

This study investigated the relative effectiveness of letter-based versus syllable-based measures of speech rate and articulation rate in predicting the articulation score, prosody fluency, and rating sum using "English speech data of Koreans for education" from AI Hub. We extracted and analyzed 900 utterances from the training data, including three balanced age groups (13, 19, and 26 years old). The study built three models that best predicted the pronunciation assessment scores using linear mixed-effects regression and compared the predicted scores with the actual scores from the validation data (n=180). The correlation coefficients between them were also calculated. The findings revealed that syllable-based measures of speech and articulation rates were more effective than letter-based measures in all three pronunciation assessment categories. The correlation coefficients between the predicted and actual scores ranged from .65 to .68, indicating the models' good predictive power. However, it remains inconclusive whether speech rate or articulation rate is more effective.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.701-708
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• 2024

The advent of transformer architectures in deep learning has been a major breakthrough in natural language processing research. Object name recognition is a branch of natural language processing and is an important research area for tasks such as information retrieval. It is also important in the biomedical field, but the lack of Korean biomedical corpora for training has limited the development of Korean clinical research using AI. In this study, we built a new biomedical corpus for Korean biomedical entity name recognition and selected language models pre-trained on a large Korean corpus for transfer learning. We compared the name recognition performance of the selected language models by F1-score and the recognition rate by tag, and analyzed the errors. In terms of recognition performance, KlueRoBERTa showed relatively good performance. The error analysis of the tagging process shows that the recognition performance of Disease is excellent, but Body and Treatment are relatively low. This is due to over-segmentation and under-segmentation that fails to properly categorize entity names based on context, and it will be necessary to build a more precise morphological analyzer and a rich lexicon to compensate for the incorrect tagging.

• Korean Journal of Poultry Science
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• v.32 no.2
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• pp.113-123
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• 2005

Avian influenza (AI) virus (AIV) is distributed worldwide and it has been isolated from various species of wild and domestic birds. AI transfers with high speed and shows diverse pathogenicity syndroms. In Korea, several low Pathogenic AIV, H9N2, have been isolated from the commercial farms with severe decrease of egg production and mortality resulted in severe economic loss since 1996. Therefore, it has been requested to develop AI vaccines to prevent clinical signs and economic losses from the field infection of AIV. To develop a killed vaccine that efficiently prevents low pathogenic AIV (H9N2), evaluation on the pathogenicity and selection of an inactivator for H9N2 is taking place and is being tested safety and immunogenicity of vaccine produced. Based on the pathogenicity test and viral reisolation test, the ADL0401 isolate is the characteristic low pathogenic AIVs and has fairly similar biologic functions compared with MS96 which is the official low pathogenic AIV (H9N2) and one of the predominant AIV isolated from poultry farms in Korea. In antigenicity tests, the ADL0401 and MS96 virus have no significant antigenic difference. In inactivation tests, the ADL0401 isolates can be easily inactivated with $0.1\%$ Formalin at $37^{\circ}C$ within 1 hour with a little decrease of HA titer. The vaccine developed in the present report has no harmful effect on bird and forms good immune capability. Therefore, the isolates, ADL0401 can be used for a killed vaccine which can reduce the clinical signs and viral shedding in the birds infected with H9N2 low pathogenic AIVs.