• Journal of Energy Engineering
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• v.26 no.3
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• pp.11-29
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• 2017

In recent years, the use of artificial intelligence technology such as deep neural net machine learning(deep learning) is becoming an effective and practical option in industrial manufacturing process. This study focuses on recent deep learning development environments and their applications in the manufacturing field.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.86-93
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• 2022

To combine the science and technology creativity necessary in the era of the 4th Industrial Revolution, it is necessary to cultivate talents who can discover new knowledge and create new values by combining various knowledge with self-directed mathematical competencies. This research attempted to lay the foundation for the curriculum for fostering future creative convergence talent by preparing, executing, and reflecting on the learning plan after learners themselves understand their level and status through self-directed learning. Firstly, We would like to present a teaching-learning plan based on the essential capabilities of the future society, where the development of a curriculum based on mathematics curriculum and intelligent informatization are accelerated. Secondly, an educational design model system diagram was presented to strengthen the self-directed learning ability of mathematics subjects in the electronic engineering curriculum. Consequently, through a survey, we would like to propose the establishment of an educational system necessary for the 4th industry by analyzing learning ability through self-directed learning teaching methods of subjects related to mathematics, probability, and statistics.

• Educational Technology International
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• v.24 no.1
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• pp.81-114
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• 2023

The study aimed to qualitatively examine middle school students' engagement in distance instruction during the COVID-19 pandemic. The participants comprised 119 students from a girls' middle school in Seoul, South Korea. To gain an in-depth understanding of the students' experiences, we collected their reflective journals, which included structured items about their learning engagement at three timepoints in 2020: April, July, and December. The following are the results: 10 themes and 18 concepts were derived, and they were integrated into causal conditions (sudden transition due to COVID-19), contextual condition (technology readiness, school education context), central phenomena (high level of behavioral engagement, low emotional engagement), interventional conditions (recognizing the potential of online learning, situational awareness about COVID-19 and online learning), action/interaction phenomena (development and use of self-regulated learning strategies), and consequences (changes in practices and perception towards online learning). Based on the findings, engagement patterns of the participants were classified into five types: proactive, conservative, receptive, reactive, passive learners. The present study demonstrated important findings that are essential for the improvement and development of engaging online learning strategies in the future.

• Educational Technology International
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• v.18 no.2
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• pp.131-157
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• 2017

The purposes of the study were to investigate the participation of artists in paper circuit computing learning and to conduct an in-depth study on the formation and development of practical knowledge. To do this, we selected as research participants six artists who participated in the learning program of an art museum, and used various methods such as pre-open questionnaires, participation observation, and individual interviews to collect data. The collected data were analyzed based on community of practice theory. Results showed that the artists participated in the learning based on a desire to use new technology or find a new work production method for interacting with their audiences. In addition, the artists actively formed practical knowledge in the curriculum and tried to apply paper circuit computing to their works. To continuously develop the research, participants formed a study group or set up a practical goal through planned exhibitions. The results of this study can provide implications for practical approaches to, and utilization of, paper circuit computing.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.56-63
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• 2023

I proposed the architectural guideline for educational environment based on design thinking approach to integrate and enhance learners' activities and achievements. The physical environment of design education learning space should be applied by teaching methods and learning activities, especially for STEAM-based convergent education, the architectural space conditions should support the design process based on design thinking. The learning environment conditions influence design education with physical design factors and learners' communication, and the flexible environment based on design thinking, which is crucial for design education. The 3 steps of design thinking experiences also allow students to learn the context of ideas, skills and outcomes. Therefore, I argued that the learning surrounding based on design thinking needs flexible and mobile, connected, integrated, organized, and team-focused environments to support learners' understanding, participation, and collaboration, and to achieve the design process based on research findings. For spaces for convergent learning environments based on design thinking, common design principles should be reviewed, such as coexistence with technology, safety and security, transparency and spatial extension, multi-purpose space and outdoor learning.

• Genomics & Informatics
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• v.17 no.3
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• pp.30.1-30.4
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• 2019

Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.606-610
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• 2009

As the technology of tele communication develops, learners want their study environment free from time and place limitation. The paper will reformulate the existing learning contents using the DMB broadcasting technology, enhancing the qualities of learning by transforming analog learning environments into digital ones. The paper will also present Multimedia-based learning patterns using one of the most notable technologies, DMB broadcasting technology.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.119-128
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• 2020

Object detection is an important field of computer vision and is applied to applications such as security, autonomous driving, and face recognition. Recently, as the application of artificial intelligence technology including deep learning has been applied in various fields, it has become a more powerful tool that can learn meaningful high-level, deeper features, solving difficult problems that have not been solved. Therefore, deep learning techniques are also being studied in the field of object detection, and algorithms with excellent performance are being introduced. In this paper, a deep learning-based object detection algorithm used to detect multiple objects in an image is investigated, and future development directions are presented.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.6
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• pp.481-493
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• 2020

Deep learning models, which imitate the function of human brain, have drawn attention from many engineering fields (mechanical, agricultural, and computer engineering etc). The major advantages of deep learning in engineering fields can be summarized by objects detection, classification, and time-series prediction. As well, it has been applied into environmental science and engineering fields. Here, we compiled our previous attempts to apply deep learning models in water-environment field and presented the future opportunities.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.358-368
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• 2022

Social media is a window for everyone, individuals, communities, and companies to spread ideas and promote trends and products. With these opportunities, challenges and problems related to security, privacy and rights arose. Also, the data accumulated from social media has become a fertile source for many analytics, inference, and experimentation with new technologies in the field of data science. In this chapter, emphasis will be given to methods of trend analysis, especially ensemble learning methods. Ensemble learning methods embrace the concept of cooperation between different learning methods rather than competition between them. Therefore, in this chapter, we will discuss the most important trends in ensemble learning and their applications in analysing social media data and anticipating the most important future trends.