• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.212-215
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• 2022

In the era of so-called digital transformation today, the need for the construction and utilization of big data in various fields has increased. Today, a lot of data is produced and stored in a digital device and media-friendly manner, but the production and storage of data for a long time in the past has been dominated by print books. Therefore, the need for Optical Character Recognition (OCR) technology to utilize the vast amount of print books accumulated for a long time as big data was also required in line with the need for big data. In this study, a system for digitizing the structure and content of a document object inside a scanned book image is proposed. The proposal system largely consists of the following three steps. 1) Recognition of area information by document objects (table, equation, picture, text body) in scanned book image. 2) OCR processing for each area of the text body-table-formula module according to recognized document object areas. 3) The processed document informations gather up and returned to the JSON format. The model proposed in this study uses an open-source project that additional learning and improvement. Intelligent OCR proposed as a system in this study showed commercial OCR software-level performance in processing four types of document objects(table, equation, image, text body).

• Journal of The Korean Association For Science Education
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• v.43 no.6
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• pp.573-582
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• 2023

This study aimed to explore the potential use of artificial intelligence in science education for gifted students by analyzing the structure of abstracts written by students at a gifted science academy and comparing the performance of various elements extracted using AI. The study involved an analysis of 263 graduation theses from S Science High School over five years (2017-2021), focusing on the frequency and types of background, objectives, methods, results, and discussions included in their abstracts. This was followed by an evaluation of their accuracy using AI classification methods with fine-tuning and prompts. The results revealed that the frequency of elements in the abstracts written by gifted students followed the order of objectives, methods, results, background, and discussions. However, only 57.4% of the abstracts contained all the essential elements, such as objectives, methods, and results. Among these elements, fine-tuned AI classification showed the highest accuracy, with background, objectives, and results demonstrating relatively high performance, while methods and discussions were often inaccurately classified. These findings suggest the need for a more effective use of AI, through providing a better distribution of elements or appropriate datasets for training. Educational implications of these findings were also discussed.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.137-144
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• 2023

Cyberattack technology is evolving to an unpredictable degree, and it is a situation that can happen 'at any time' rather than 'someday'. Infrastructure that is becoming hyper-connected and global due to cloud computing and the Internet of Things is an environment where cyberattacks can be more damaging than ever, and cyberattacks are still ongoing. Even if damage occurs due to external influences such as cyberattacks or natural disasters, intelligent self-recovery must evolve from a cyber resilience perspective to minimize downtime of cyber assets (OS, WEB, WAS, DB). In this paper, we propose an intelligent self-recovery technology to ensure sustainable cyber resilience when cyber assets fail to function properly due to a cyberattack. The original and updated history of cyber assets is managed in real-time using timeslot design and snapshot backup technology. It is necessary to secure technology that can automatically detect damage situations in conjunction with a commercialized file integrity monitoring program and minimize downtime of cyber assets by analyzing the correlation of backup data to damaged files on an intelligent basis to self-recover to an optimal state. In the future, we plan to research a pilot system that applies the unique functions of self-recovery technology and an operating model that can learn and analyze self-recovery strategies appropriate for cyber assets in damaged states.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.51-63
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• 2023

The purpose of this study is to develop and implement a secondary teacher training program based on the TPACK model to strengthen the capacity of teachers of youth entrepreneurship education in the context of the increasing importance of entrepreneurship as a future competency, and to provide theoretical and practical implications based on it. To this end, a teacher training program was developed through the process of analysis, design, development, implementation, and evaluation based on the ADDIE model, and 22 secondary school teachers in Gangwon Province were trained and the effectiveness and validity were analyzed. First, the results of the paired sample t-test of TPACK in entrepreneurship education conducted before and after the program showed statistically significant improvements in all sub-competencies. Second, the satisfaction survey of the training program showed that the overall satisfaction was high with M=4.83. Third, the validity of the program was reviewed by three experts, and it was found to be highly valid with a validity of M=5.0, usefulness of M=4.7, and universality of M=5.0. Based on the results, it is suggested that in order to expand entrepreneurship education, opportunities for teachers' holistic capacity building such as TPACK should be expanded, teachers' understanding and practice of backward design should be promoted, and access to various resources that can be utilized in entrepreneurship education should be improved.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.63-74
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• 2024

In agriculture, frost can be devastating, which is why observation and forecasting are so important. According to a recent report analyzing frost observation data from the Korea Meteorological Administration, despite global warming due to climate change, the late frost date in spring has not been accelerated, and the frequency of frost has not decreased. Therefore, it is important to automate and continuously operate frost observation in risk areas to prevent agricultural frost damage. In the existing frost observation using leaf wetness sensors, there is a problem that the reference voltage value fluctuates over a long period of time due to contamination of the observation sensor or changes in the humidity of the surrounding environment. In this study, a datalogger program was implemented to automatically solve these problems. The established frost observation system can stably and automatically accumulate time-resolved observation data over a long period of time. This data can be utilized in the future for the development of frost diagnosis models using machine learning methods and the production of frost occurrence prediction information for surrounding areas.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.112-118
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• 2009

This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.217-225
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• 2024

To foster the core talents of the future, the development of diverse and substantial SW·AI education programs is required, and a systematic system that can assist public education in SW and AI must be established. In this study, we develop and combine SW·AI education modules to construct a SW and AI education program applicable to public education. We also establish a systematic education system and provide sustainable SW·AI education to elementary, middle, and high school students through 'Job's Garage Camp' based on various sharing platforms. By creating a sustainable follow-up educational environment, students are encouraged to continue their self-directed learning of SW and AI. As a result of conducting a pre-post survey of students participating in the 'Job's Garage Camp', the post-survey values improved compared to the pre-survey values in all areas of 'interest', 'understanding and confidence', and 'career aspirations'. Based on these results, it can be confirmed that students had a universal positive perception and influence on SW and AI. Therefore, if the operation case of 'Job's Garage Camp' is improved and expanded, it can be presented as a standard model applicable to other SW and AI education programs in the future.

• Journal of The Korean Association For Science Education
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• v.27 no.9
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• pp.854-869
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• 2007

In this study, we investigated secondary science teachers' internalization of constructivistic science teaching who participated in a collaborative program between teachers and researchers designed by researchers according to constructivist views. The program consisted of lecture, workshop, and small group activities. New trends in science education and framework for science teaching were introduced during lectures, and understanding about the framework were deepened by analyzing school science classes recorded during workshops. In small group activities, participating teachers and researchers cooperated to design science lesson plans using science teaching frameworks. Five secondary science teachers participated in collaborative workshops. Collaborative programs were video-taped. Semi-structured interviews were conducted before and after workshops. All data recorded were transcribed and analyzed. In the process of internalization, participating teachers attended on different parts. Various and discernable factors such as there own background, beliefs, values, and school context produced tensions with or facilitated internalization of constructivistic science teaching. Teaching experiences and student understanding affected teachers' lesson planning activities. Teachers also showed different understandings on inquiry, application, and model from the framework, and they interpret those concepts in the framework based on their prior understanding. They perceived that too much content should be dealt within relatively limited time. Therefore, they tended to separate science class into two parts when developing science lessons: explaining science content by lecture and science laboratory as a constructivistic activity. The results of the study provide meaningful implications to the constructivist teacher education and professional development.

• Clinical and Experimental Pediatrics
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• v.52 no.7
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• pp.824-831
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• 2009

Purpose : We aimed to investigate the efficacy of and functional recovery after intracerebral transplantation of different doses of mouse mesenchymal stem cells (mMSCs) in immature rat brain with hypoxic-ischemic encephalopathy (HIE). Methods : Postnatal 7-days-old Sprague-Dawley rats, which had undergone unilateral HI operation, were given stereotaxic intracerebral injections of either vehicle or mMSCs and then tested for locomotory activity in the 2nd, 4th, 6th, and 8th week of the stem cell injection. In the 8th week, Morris water maze test was performed to evaluate the learning and memory dysfunction for a week. Results : In the open field test, no differences were observed in the total distance/the total duration (F=0.412, P=0.745) among the 4 study groups. In the invisible-platform Morris water maze test, significant differences were observed in escape latency (F=380.319, P<0.01) among the 4 groups. The escape latency in the control group significantly differed from that in the high-dose mMSC and/or sham group on training days 2-5 (Scheffe's test, P<0.05) and became prominent with time progression (F=6.034, P<0.01). In spatial probe trial and visible-platform Morris water maze test, no significant improvement was observed in the rats that had undergone transplantation. Conclusion : Although the rats that received a high dose of mMSCs showed significant recovery in the learning-related behavioral test only, our data support that mMSCs may be used as a valuable source to improve outcome in HIE. Further study is necessary to identify the optimal dose that shows maximal efficacy for HIE treatment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.262-268
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• 2017

The energy consumption of buildings is approximately 20.5% of the total energy consumption, and the interest in energy efficiency and low consumption of the building is increasing. Several studies have performed energy analysis and evaluation. Energy analysis and evaluation are effective when applied in the initial design phase. In the initial design phase, however, the energy performance is evaluated using general level information, such as glazing area and surface area. Therefore, the evaluation results of the detailed design stage, which is based on the drawings, including detailed information of the materials and facilities, will be different. Thus far, most studies have reported the analysis and evaluation at the detailed design stage, where detailed information about the materials installed in the building becomes clear. Therefore, it is possible to improve the accuracy of the energy environment analysis if the energy environment information generated during the life cycle of the building can be established and accurate information can be provided in the analysis at the initial design stage using a probability / statistical method. On the other hand, historical data on energy use has not been established in Korea. Therefore, this study performed energy environment analysis to construct the energy environment historical data. As a result of the research, information classification system, information model, and service model for acquiring and providing energy environment information that can be used for building lifecycle information of buildings are presented and used as the basic data. The results can be utilized in the historical data management system so that the reliability of analysis can be improved by supplementing the input information at the initial design stage. If the historical data is stacked, it can be used as learning data in methods, such as probability / statistics or artificial intelligence for energy environment analysis in the initial design stage.