• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.49-54
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• 2021

With the recent development of smart healthcare technology, interest in daily diseases is increasing. However, healthcare data has an imbalance between positive and negative data. This is caused by the difficulty of collecting data because there are relatively many people who are not patients compared to patients with certain diseases. Data imbalances need to be adjusted because they affect performance in ongoing learning during disease prediction and analysis. Therefore, in this paper, We replace missing values through multiple imputation in detection models to determine whether they are prevalent or not, and resolve data imbalances through over-sampling. Based on AutoML using preprocessed data, We generate several models and select top 3 models to generate ensemble models.

• Journal of Engineering Education Research
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• v.24 no.6
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• pp.79-85
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• 2021

In a future society where cutting-edge science technology such as artificial intelligence becomes commonplace, the demand for talented people with basic knowledge of mathematics and science is expected to increase continuously, and the educational infrastructure suitable for the characteristics of future generations is still insufficient. In particular, in the case of students taking convergence courses including practical training, there was a problem in communication with the instructor. In this study, we looked at the current status of distance learning at domestic universities that came suddenly due to the global pandemic of COVID-19. In addition, a case study of the use of technology was conducted to facilitate the interaction between instructors and learners through case analysis of distance classes in convergence subjects. Therefore, this study aims to introduce the case of developing lecture contents for smooth convergence education in a non-face-to-face educational environment targeting the developed AI convergence courses and applying them to the education of enrolled students.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.466-474
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• 2023

In recent years, the government has strongly promoted multi-purpose utilization of paddy field. However, poor drainage causes waterlogging stress in upland crops, requiring subsurface drainage technology, resulting in high installation and management costs. To address this issue, a low-cost and high-efficiency technique was developed that utilizes wasted coir substrates which have characteristics of high porosity and good drainage, for upland crop cultivation in paddy fields. Soybeans were grown in both paddy soil and wasted coir slab with two planting densities (80×20 cm and 60×20 cm). The results showed that the coir substrates had better performance than the paddy soil in terms of soil physical and chemical properties and the growth and yield of upland crops are improved. The treatments using wasted coir substrate showed a 41.4% increase in yield and a 21.3% increase in protein content compared to PS treatment. Our findings demonstrate that recycling waste coir substrates to grow upland crops is a positive cultivation strategy to solve some drainage problems in paddy fields. This approach offers a sustainable solution for upland crop production while also addressing the issue of waste management in agriculture.

• Journal of the Korean earth science society
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• v.44 no.4
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• pp.331-341
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• 2023

The goal of this study was to examine the PCK required for science teachers and PCK required for university teacher educators in terms of school science knowledge, science teaching and learning, and the role of science educators, which are the main axes of science education in future schools, and to explore the relationship between them. This study is a follow-up to a previous stage of research that explored the prospects for changes in schools in the future (2040-2050) in terms of school knowledge, educational methods, and teacher roles. Based on in-depth interviews, qualitative and semantic network analyses were conducted to derive and compare the characteristics of PCK and PCK. As for the main research results, science teacher PCK in future schools should include expertise in organizing science classes centered on convergence topics, expertise in digital platforms and ICT use, and expertise in building a network of learning communities and resources, as part of the expertise of human teachers differentiated from AI. Teacher educators' PCK includes expertise in the research and development of T-L methods using AI, expertise in the knowledge construction process and practice, and expertise in developing preservice teachers' research competencies. Discussed in the conclusion is the change in teacher PCK and teacher educator PCK with changes in science knowledge, such as convergence-type knowledge and cognition-value integrated knowledge; and the need to emphasize values, attitudes, and ethical judgments for the coexistence of humans and non-humans as school science knowledge in the post-humanism future society.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.93-106
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• 2021

Blenderbot 2.0 is a dialogue model representing open domain chatbots by reflecting real-time information and remembering user information for a long time through an internet search module and multi-session. Nevertheless, the model still has many improvements. Therefore, this paper analyzes the limitations and errors of BlenderBot 2.0 from three perspectives: model, data, and dialogue. From the data point of view, we point out errors that the guidelines provided to workers during the crowdsourcing process were not clear, and the process of refining hate speech in the collected data and verifying the accuracy of internet-based information was lacking. Finally, from the viewpoint of dialogue, nine types of problems found during conversation and their causes are thoroughly analyzed. Furthermore, practical improvement methods are proposed for each point of view, and we discuss several potential future research directions.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.979-987
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• 2021

Multiple Sclerosis (MS) can be early diagnosed by detecting lesions in brain magnetic resonance images (MRI). Unsupervised anomaly detection methods based on autoencoder have been recently proposed for automated detection of MS lesions. However, these autoencoder-based methods were developed only for 2D images (e.g. 2D cross-sectional slices) of MRI, so do not utilize the full 3D information of MRI. In this paper, therefore, we propose a novel 3D autoencoder-based framework for detection of the lesion volume of MS in MRI. We first define a 3D convolutional neural network (CNN) for full MRI volumes, and build each encoder and decoder layer of the 3D autoencoder based on 3D CNN. We also add a skip connection between the encoder and decoder layer for effective data reconstruction. In the experimental results, we compare the 3D autoencoder-based method with the 2D autoencoder models using the training datasets of 80 healthy subjects from the Human Connectome Project (HCP) and the testing datasets of 25 MS patients from the Longitudinal multiple sclerosis lesion segmentation challenge, and show that the proposed method achieves superior performance in prediction of MS lesion by up to 15%.

• Korean Journal of Poultry Science
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• v.50 no.3
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• pp.187-191
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• 2023

Korean native ducks (KNDs) continue to have a high preference from consumers due to their excellent meat quality and taste characteristics. However, due to low productivity and fixed plumage color phenotype, it could not secure a large share in the domestic market compared to imported species. In order to improve the market share of KNDs, the genetic characteristics of the breed should be identified and used for improvement and selection. Therefore, this study was conducted to identify the genetic information of colored and white KNDs using next-generation resequencing data and screening for differences between the two groups. As a result of the analysis, the genetic variants that showed significant differences between the colored and white KND groups were mainly identified as mutations related to tyrosine activity. The variants were located in the genes that affect melanin synthesis and regulation, such as EGFR, PDGFRA, and DDR2, and these were reported as the candidate genes related to plumage pigmentation in poultry. Therefore, the results of this study are expected to be useful as a basis for understanding and utilizing the genetic characteristics of KNDs for genetic improvement and selection of white broiler KNDs.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.519-524
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• 2021

Recently, the fourth industrial revolution has become an area of interest to many countries, mainly in major advanced countries. Artificial intelligence technology, the core technology of the fourth industrial revolution, is developing in a form of convergence in various fields and has a lot of influence on the edutech field to change education innovatively. This paper builds an experimental environment using the DTW speech recognition algorithm and deep learning on various native and non-native data. Furthermore, through comparisons with CNN algorithms, we study non-native speakers to correct them with similar pronunciation to native speakers by measuring the similarity of English pronunciation.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.137-147
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• 2018

To prepare for and implement policies for the Fourth Industrial Revolution, which is characterized by convergence, super-connectivity, and AI, this study summarized the effects and characteristics of individual technologies on our society and discussed the issues with humanity and social science perspectives. As a result, in terms of AI technology, the issues of job losses, project-type works, basic income and robot taxes, accountability of AI, and algorithm inequality were dealt with. Security, cyber hacking and privacy infringement issues were highlighted in big-data technology. In the part of block-chain and bioengineering, the society of decentralization, the concentration, digital divide, and ethical issues were discussed. On-demand economic aspects highlighted the problems of civil ethics and human commercialization. Lastly, the development of VR is discussed including side effects such as cyber-syndrom, avoidance of reality, and so on.