• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.2
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• pp.131-138
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• 2019

Carbonylated proteins (CPs) are synthesized by the chemical reaction of basic amino acid residues in proteins with aldehyde compounds yielded by lipid peroxidation. CPs are excited by a range of light from UVA to blue light, and resulted in the generation of superoxide anion radicals ($^{\cdot}O_2{^-}$) by photosensitizing reaction. Then, they CPs induce new protein carbonylation in stratum corneum through ROS generation. Furthermore, the superoxide anion radicals produce CPs in the stratum corneum (SC) through lipid peroxidation and finally affects skin conditions including color and moisture functions. The purpose of this study was to investigate the relationship between the production of stratum corneum carbonylated protein (SCCP) and the skin elasticity. 46 healthy female Koream at the ages of 30 ~ 50 years old were participated in this study for 8 weeks. The skin test was experiment conducted into two groups; placebo group (N = 23) used cream that did not contain active ingredients, and the other group (N = 23) used cream containing the elasticity improving ingredients. Test areas were the crow 's feet and the cheek. Various non-invasive methods were carried out to measure biophysical parameters on human skin indicating that dermis density and skin wrinkle were measured by using DUB scanner and Primos premium, respectively. Skin elasticity were measured using dermal torque meter (DTM310) and balistometer (BLS780). SCCP was assessed in a simple and non-invasive method using skin surface biopsy on the cheek of the subject. The amount of SCCP was determined using image analysis. All measurements were taken at 0, 4 and 8 8week. Results revealed that the amount of CP in SC was reduced when the skin wrinkle and skin elasticity related parameters were improved. This indicates that the correlation between the elasticity improvement and the amount of CP can be used as a anti-aging indicator and applicable to the skin clinical test for the measurement of skin aging in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.103-113
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• 2019

Spatial resolution is still one of the most important parameters for evaluating image quality. In this study, we propose an approach to evaluate spatial resolution and MTF(Modulation Transfer Function) using bar target and Siemens star chart as a part of quality evaluation for UAV images. To this end, images were taken with a fixed-wing eBee(Canon IXUS) at the flight height of 130m and 260m, and with a rotary-wing GD-800(SONY NEX-5N) at flight height of 130m, with a Phantom 4 pro(FC 6310) at flight height of 90m, respectively. Spatial resolution was measured on orthoimages produced from this data. Results show that the resolution measured on the Siemens star and bar target was accurately degraded in proportion to the flight height regardless of the cameras. In the words, the spatial resolution of images taken at the same altitude of 130m with the eBee(Canon IXUS) and the GD-800(SONY NEX-5N) equipped with different cameras was the same as 4.1cm, and that of the eBee(Canon IXUS) at 260m was 8.0cm. In addition, the resolution measured on the Siemens star was about 1~2cm lower than that of the bar target at every flight height. The general tendency was also found to be proportional to the flight height in the measurement of the ${\sigma}_{MTF}$ from MTF, which simultaneously represents the resolution and contrast information of the image. However, at the same altitude of 130m, the ${\sigma}_{MTF}$ of the GD-800(SONY NEX-5N) is 0.36 and the eBee(Canon IXUS) is 0.59, which shows that the GD-800(SONY NEX-5N) has better camera performance. It is expected that study results will contribute to the analysis of spatial resolution of UAV images and to improve the reliability of quality.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.297-309
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• 2023

The degradation of coastal ecosystems and fishery environments is accelerating due to the recent phenomenon of invertebrate grazers. To effectively monitor and implement preventive measures for this phenomenon, the adoption of remote sensing-based monitoring technology for extensive maritime areas is imperative. In this study, we compared and analyzed the robustness of deep learning-based object detection modelsfor detecting and monitoring invertebrate grazersfrom underwater videos. We constructed an image dataset targeting seven representative species of invertebrate grazers in the coastal waters of South Korea and trained deep learning-based object detection models, You Only Look Once (YOLO)v7 and YOLOv8, using this dataset. We evaluated the detection performance and speed of a total of six YOLO models (YOLOv7, YOLOv7x, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) and conducted robustness evaluations considering various image distortions that may occur during underwater filming. The evaluation results showed that the YOLOv8 models demonstrated higher detection speed (approximately 71 to 141 FPS [frame per second]) compared to the number of parameters. In terms of detection performance, the YOLOv8 models (mean average precision [mAP] 0.848 to 0.882) exhibited better performance than the YOLOv7 models (mAP 0.847 to 0.850). Regarding model robustness, it was observed that the YOLOv7 models were more robust to shape distortions, while the YOLOv8 models were relatively more robust to color distortions. Therefore, considering that shape distortions occur less frequently in underwater video recordings while color distortions are more frequent in coastal areas, it can be concluded that utilizing YOLOv8 models is a valid choice for invertebrate grazer detection and monitoring in coastal waters.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.24-35
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• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• The Korean Journal of Nuclear Medicine
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• v.39 no.6
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• pp.413-420
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• 2005

Purpose: Neuroreceptor PET studies require 60-120 minutes to complete and head motion of the subject during the PET scan increases the uncertainty in measured activity. In this study, we investigated the effects of the data-driven head mutton correction on the evaluation of endogenous dopamine release (DAR) in the striatum during the motor task which might have caused significant head motion artifact. Materials and Methods: $[^{11}C]raclopride$ PET scans on 4 normal volunteers acquired with bolus plus constant infusion protocol were retrospectively analyzed. Following the 50 min resting period, the participants played a video game with a monetary reward for 40 min. Dynamic frames acquired during the equilibrium condition (pre-task: 30-50 min, task: 70-90 min, post-task: 110-120 min) were realigned to the first frame in pre-task condition. Intra-condition registrations between the frames were performed, and average image for each condition was created and registered to the pre-task image (inter-condition registration). Pre-task PET image was then co-registered to own MRI of each participant and transformation parameters were reapplied to the others. Volumes of interest (VOI) for dorsal putamen (PU) and caudate (CA), ventral striatum (VS), and cerebellum were defined on the MRI. Binding potential (BP) was measured and DAR was calculated as the percent change of BP during and after the task. SPM analyses on the BP parametric images were also performed to explore the regional difference in the effects of head motion on BP and DAR estimation. Results: Changes in position and orientation of the striatum during the PET scans were observed before the head motion correction. BP values at pre-task condition were not changed significantly after the intra-condition registration. However, the BP values during and after the task and DAR were significantly changed after the correction. SPM analysis also showed that the extent and significance of the BP differences were significantly changed by the head motion correction and such changes were prominent in periphery of the striatum. Conclusion: The results suggest that misalignment of MRI-based VOI and the striatum in PET images and incorrect DAR estimation due to the head motion during the PET activation study were significant, but could be remedied by the data-driven head motion correction.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.484-495
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• 2020

This study is a mixed surfactant (MimicLipid-MSM1000) that forms the same structure as that of the stratum corneum, sucrose distearate, polyglyceryl-2 dioleate, fermented squalane, ergosterol, 10-hydroxystearic acid, mixture consisting of was synthesized. When using 2~5 wt% of this mixed surfactant, it was possible to make an artificial skin mimetic that forms a multi-layer lamellar structure of 5~30 layers. An emulsion was prepared using this mixed surfactant, and a multi-layered lamellar phase was formed and analyzed mechanically. The appearance of this surfactant was a light brown hard wax, the hydrophilic lipophilic balance (HLB) was 12.53, the critical parameter value was 0.987, and the acid value was 0.13. Stability according to pH change was also stable in acidic (3.8), neutral (7.2) and alkaline (10.8). The particle size of the liquid crystal was found to be the most stable maltese cross lamellar crystalline droplet at 5~25mm. The size of the emulsified particles according to the change in the speed of the homo agitator is 2500 rpm (17.9mm±2.6mm), 3500rpm (12.5mm±2.1mm), 4500rpm (6.2mm±1.8mm) particles were formed. Liquid crystal forming particles were observed through a polarization microscope, and the formation structure of the liquid crystal was precisely analyzed with a scanning electron microscope (cryo-SEM). As an application field, it is expected that it will be widely applicable to the development of various prescriptions, such as various skin care cosmetics, makeup care cosmetics, and scalp protection cosmetics, by using a skin-mimicking surfactant.