• Korean Journal of Clinical Laboratory Science
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• v.49 no.3
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• pp.300-307
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• 2017

Electroencephalography (EEG) is distinct from other medical imaging tests in that it is a functional test that helps to diagnosis disorders related to the brain, such as epilepsy. The most important abilities for a medical technologist when performing an EEG are knowing the exact location of the electrode and recording the EEG wave clearly, except for artifacts. Although theoretical education and practical training are both included in the curriculum for improving these abilities, sufficient practical training has been lacking due to problems like expensive equipment and insufficient practical training time. We try to solve these issues by manufacturing the training 10-20 electrode system and by estimating the efficiency and satisfaction of the training 10-20 electrode system through a questionnaire. The time required for practical training using this system was $43.58{\pm}9.647min$, which proved to be efficient. The satisfaction score of participants who experienced curriculum practical training was improved from $7.21{\pm}2.285$ to $9.46{\pm}1.166$. Based on these findings, it is considered that practical training via the use of the training 10-20 electrode system will solve the problems, such as lack of equipment and insufficient practical training time. Nonetheless, to further improve the training 10-20 electrode system, it must overcome the limitations of developing a device capable of checking the actual brain waves and validating the exact location of electrode attachment.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.