• Journal of the Society of Cosmetic Scientists of Korea
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• v.50 no.2
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• pp.119-129
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• 2024

The cutaneous lymphatic system in humans plays a crucial role in draining interstitial fluid and activating the immune system. Environmental factors, such as ultraviolet light and natural aging, often affect structural changes of such lymphatic vessels, causing skin dysfunction. However, some limitations still exist because of no alternatives to animal testing. To better understand the skin lymphatic system, a biomimetic microfluidic platform, skin-lymph-on-a-chip, was fabricated to develop a novel in vitro skin lymphatic model of humans and to investigate the molecular and physiological changes involved in lymphangiogenesis, the formation of lymphatic vessels. Briefly, the platform involved co-culturing differentiated primary normal human epidermal keratinocytes (NHEKs) and dermal lymphatic endothelial cells (HDLECs) in vitro. Based on our system, Lymphanax^TM, which is a condensed Panax ginseng root extract obtained through thermal conversion for 21 days, was applied to evaluate the lymphangiogenic effect, and the changes in molecular factors were analyzed using a deep-learning-based algorithm. Lymphanax^TM promoted healthy lymphangiogenesis in skin-lymphon-a-chip and indirectly affected HDELCs as its components rarely penetrated differentiated NHEKs in the chip. Overall, this study provides a new perspective on Lymphanax^TM and its effects using an innovative in vitro system.

• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.323-338
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• 2023

TV advertising with deep analysis of watching pattern of audiences is important to set-top box audience targeting. Applying session-based recommendation model(SBR) to internet commercial, or recommendation based on searching history of user showed its effectiveness in previous studies, but applying SBR to the TV advertising was difficult in South Korea due to data unavailabilities. Also, traditional SBR has limitations for dealing with user preferences, especially in data with user identification information. To tackle with these problems, we first obtain set-top box data from three major broadcasting companies in South Korea(SKB, KT, LGU+) through collaboration with Korea Broadcast Advertising Corporation(KOBACO), and this data contains of watching sequence of 4,847 anonymized users for 6 month respectively. Second, we develop personalized session-based recommendation model to deal with hierarchical data of user-session-item. Experiments conducted on set-top box audience dataset and two other public dataset for validation. In result, our proposed model outperformed baseline model in some criteria.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.57-74
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• 2022

Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.261-282
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• 2024

This study proposed and evaluated electroencephalography (EEG)-based and eye-tracking-based methods to determine relevance by utilizing users' implicit relevance feedback while navigating content in a digital library. For this, EEG/eye-tracking experiments were conducted on 32 participants using video, image, and text data. To assess the usefulness of the proposed methods, deep learning-based artificial intelligence (AI) techniques were used as a competitive benchmark. The evaluation results showed that EEG component-based methods (av_P600 and f_P3b components) demonstrated high classification accuracy in selecting relevant videos and images (faces/emotions). In contrast, AI-based methods, specifically object recognition and natural language processing, showed high classification accuracy for selecting images (objects) and texts (newspaper articles). Finally, guidelines for implementing a digital library interface based on EEG, eye-tracking, and artificial intelligence technologies have been proposed. Specifically, a system model based on implicit relevance feedback has been presented. Moreover, to enhance classification accuracy, methods suitable for each media type have been suggested, including EEG-based, eye-tracking-based, and AI-based approaches.

• Journal of Animal Science and Technology
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• v.63 no.2
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• pp.367-379
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• 2021

The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (p < 0.05) the standing behavior of sows and also has a tendency to increase (p = 0.082) lying behavior. High dietary fiber treatment tended to increase (p = 0.064) lying and decrease (p < 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.245-259
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• 2023

The agricultural reservoir is a critical water supply system in South Korea, providing approximately 60% of the agricultural water demand. However, the reservoir faces several issues that jeopardize its efficient operation and management. To address this issues, we propose a novel deep-learning-based water level recognition model that uses CCTV image data to accurately estimate water levels in agricultural reservoirs. The model consists of three main parts: (1) dataset construction, (2) image segmentation using the U-Net algorithm, and (3) CCTV-based water level recognition using either CNN or ResNet. The model has been applied to two reservoirs G-reservoir and M-reservoir with observed CCTV image and water level time series data. The results show that the performance of the image segmentation model is superior, while the performance of the water level recognition model varies from 50 to 80% depending on water level classification criteria (i.e., classification guideline) and complexity of image data (i.e., variability of the image pixels). The performance of the model can be improved if more numbers of data can be collected.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1463-1478
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• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.309-309
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• 2018

기후변화에 따른 강우의 규모와 발생빈도 증가로 농촌유역의 홍수 피해는 지속적으로 증가하고 있다. 하지만 우리나라의 홍수 피해 저감 대책은 도시지역의 대하천 주변으로 집중되어있으며, 소하천 및 농촌유역의 홍수 피해 저감에 대한 관리와 투자 노력은 부족한 실정이다. 특히, 최근 들어 갑작스런 집중호우 등으로 인한 농촌유역 돌발홍수 피해 사례가 증가하고 있으며, 이에 대응하기 위해서는 홍수 발생 등을 신속하게 파악하기 위한 돌발홍수 예경보 시스템 개발이 필요하다. 한편, 최근 산업의 혁신과 생산성 향상을 위한 새로운 패러다임으로 4차 산업혁명이 대두되고 있으며, 빅데이터와 인공지능 (Artificial Intelligence, AI)을 비롯하여 사물인터넷 (Internet of Things, IoT), 드론, 슈퍼컴퓨팅 등의 이른바 4차 산업혁명 기술을 활용한 연구가 수행되고 있다. 본 연구에서는 기후변화에 따른 농촌유역 홍수 피해를 저감하고 또한 사전에 대비하기 위해 빅데이터와 인공지능 등 4차 산업혁명 기술을 적용한 농촌유역 돌발홍수 예경보 시스템을 개발하고 그 적용성을 평가하고자 한다. 우선, 농촌유역의 홍수와 관련된 빅데이터 (기상 자료, 수문 자료, 기후변화 자료, 농업용 수리구조물 자료 등)를 토대로 정형 빅데이터와 비정형 빅데이터를 구분 추출하고 이를 연계 해석할 수 있는 시스템을 개발하였다. 추출한 정형 및 비정형 빅데이터를 활용하여 딥러닝을 기반으로 농촌유역의 홍수를 예측하고 홍수 예경보 기준에 따른 평가를 수행할 수 있는 시스템을 개발하였다. 과거 강우사상을 홍수 예경보 시스템에 적용하여 홍수 모의 결과를 도출하였으며, 재해연보 등과 비교 분석하여 시스템의 적용성을 분석하였다.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.8-13
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• 2019

Recently, In line with the recent trend of the fourth industrial revolution, many companies and institutions have been increasingly applying automated technologies using artificial intelligence to various tasks. Particularly, due to the government's 52-hour workweek system, companies are increasingly struggling with manpower management. Therefore, they are interested in RPA (Robotic Process Automation) for office environment automation for efficient manpower management. It is being introduced in the back-office business in credit card companies, bank, insurance. These RPA solutions require AI-based recognition technology, scripting technology, business software API-related technologies, and various solutions such as Automate One, Automation Anywhere, UiPath, and Blue Prism are provided. This paper analyzes and describes the technology of RPA solution, the market trend, and the efficiency of RPA adoption.