• Journal of Advanced Navigation Technology
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• v.23 no.3
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• pp.237-244
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• 2019

Since SBAS provides users with GNSS orbit, clock, and ionospheric corrections and integrity, the more precise positioning is possible. As the SBAS service area is expanded due to the development of the SBAS and the installation of the additional ground stations, there is a region where two or more SBAS messages can be received. However, the research on multi-constellation SBAS selection method has not carried out. In this study, we compared the result of positioning accuracy after applying the SBAS correction selected by using WAAS priority, EGNOS priority, or error covariance comparison method to LEO satellites in the regions where WAAS and EGNOS signals are transmitted simultaneously. When using WAAS priority method, 3D orbit error is smallest at 2.57 m. The covariance comparison method is outperform at the center of the overlap region far from each WAAS and EGNOS stations. In the eastern region near the EGNOS stations, the 3D orbit errors using EGNOS priority method is 8% smaller than the errors using the WAAS priority method.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.19-28
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• 2021

Esophagogastroduodenoscopy is a method commonly used for early diagnosis of upper gastrointestinal lesions. However, 10-20 percent of the gastric lesions are reported to be missed, due to human error. And countries including the US, the UK, and Japan, the World Endoscopy Organization (WEO) suggested guidelines about essential gastrointestinal parts to take pictures of so that all gastric lesions are observed. In this paper, we propose deep learning techniques for classification of anatomical sites, aiming for the system that informs practitioners whether they successfully did the gastroscopy without blind spots. The proposed model uses pre-processing modules and data augmentation techniques suitable for gastroscopy images. Not only does the experiment result with a maximum F1 score of 99.6%, but it also shows a error rate of less than 4% based on the actual data. Given the performance results, we found the model to be explainable with the potential to be utilized in the clinical area.

• Smart Media Journal
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• v.11 no.4
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• pp.62-68
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• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.61-70
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• 2022

Natural and synthetic forms of calcium phosphate cement (CPC) have been widely used in bone repair and augmentation. The major challenge of injectable CPC is to deliver the cells without cell death in order to regenerate new bone. The study objective was to investigate for the potential of stem cell-laden gelatin fibers containing injectable, nanocrystalline CPC to function as a delivery system. Gelatin noddle fiber method was developed to delivered cells into nCPC. Experimental groups were prepared by mixing cells with nCPC, mixing cell-laden gelatin fibers with nCPC and mixing cell-laden gelatin fibers containing BMP-2 with nCPC. Media diffusion test was conducted after dissolving the gelatin fibers. SEM examined the generated channels and delivered cell morphology. Fibers mixed with nCPC showed physical setting and hardening within 20 min after injection and showed good shape maintenances. The gelatin fibers mixed nCPC group had several vacant channels generated from the dissolved gelatin. Particularly, proliferation and attachment of the cells were observed inside of the channels. While live cells were not observed in the cell mixed nCPC group, cells delivered with the gelatin fibers into the nCPC showed good viability and increased DNA content with culture. Cell-laden gelatin fiber was a novel method for cell delivery into nCPC without cell damages. Results also indicated the osteogenic differentiation of gelatin fiber delivered cells. We suggest that the cell-laden gelatin fibers mixed with nCPC can be used as an injectable cell delivery vehicle and the addition of BMP-2 to enhances osteogenesis.

• Informatization Policy
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• v.28 no.4
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• pp.3-18
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• 2021

The 'digital divide' is regarded as a latent dysfunction that impedes the intrinsic role of information to contribute to social equality. Therefore, it has drawn great attention inside and outside of academia. The growing interest in the digital divide has been driven by the realization that it can create a serious crisis that threatens a social system compounded by existing economic, social, and cultural inequality, rather than being limited to an uneven distribution of information. In this paper (1) studies on the digital divide published since 1970 are reviewed, (2) studies noteworthy of discussion are selected to assess their academic significance, and (3) the tasks and prospects of digital divide research are explored. Although meaningful achievements have been amassed through continuous interest and efforts of the academic community, two limitations are raised; the gap between pure research and policy research that hinders the working of synthetic imagination, and the intellectual lag falling behind a rapidly changing society. In addition, it is suggested that the operation, curation, and augmentation gaps would emerge as new agenda for digital divide research in the intelligent information age.

• Journal of Periodontal and Implant Science
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• v.52 no.1
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• pp.28-38
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• 2022

Purpose: Macrophages play crucial roles as early responders to bacterial pathogens and promote/ or impede chronic inflammation in various tissues. Periodontal macrophage-induced pyroptosis results in physiological and pathological inflammatory responses. The transcription factor Dec2 is involved in regulating immune function and inflammatory processes. To characterize the potential unknown role of Dec2 in the innate immune system, we sought to elucidate the mechanism that may alleviate macrophage pyroptosis in periodontal inflammation. Methods: Porphyromonas gingivalis lipopolysaccharide (LPS) was used to induce pyroptosis in RAW 264.7 macrophages. Subsequently, we established an LPS-stimulated Dec2 overexpression cellular model in macrophages. Human chronic periodontitis tissues were employed to evaluate potential changes in inflammatory marker expression and pyroptosis. Finally, the effects of Dec2 deficiency on inflammation and pyroptosis were characterized in a P. gingivalis-treated experimental periodontitis Dec2-knockout mouse model. Results: Macrophages treated with LPS revealed significantly increased messenger RNA expression levels of Dec2 and interleukin (IL)-1β. Dec2 overexpression reduced IL-1β expression in macrophages treated with LPS. Overexpression of Dec2 also repressed the cleavage of gasdermin D (GSDMD), and the expression of caspase-11 was concurrently reduced in macrophages treated with LPS. Human chronic periodontitis tissues showed significantly higher gingival inflammation and pyroptosis-related protein expression than non-periodontitis tissues. In vivo, P. gingivalis-challenged mice exhibited a significant augmentation of F4/80, tumor necrosis factor-α, and IL-1β. Dec2 deficiency markedly induced GSDMD expression in the periodontal ligament of P. gingivalis-challenged mice. Conclusions: Our findings indicate that Dec2 deficiency exacerbated P. gingivalis LPS-induced periodontal inflammation and GSDMD-mediated pyroptosis. Collectively, our results present novel insights into the molecular functions of macrophage pyroptosis and document an unforeseen role of Dec2 in pyroptosis.

• The Journal of Internal Korean Medicine
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• v.44 no.2
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• pp.146-157
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• 2023

Restless leg syndrome is a nervous system disorder that causes an overpowering urge to move one's legs. Symptoms of restless leg syndrome usually worsen when one tries to fall asleep and can prevent sufficient sleep. Restless leg syndrome is common in patients with chronic kidney failure and can be caused or worsened by chronic kidney failure and hemodialysis. Various medications can treat restless leg syndrome, though the long-term use of medications can cause augmentation and adverse effects. In addition, the use of dopamine agonists is limited in patients with chronic kidney failure. This is because the dose of administration should be controlled for patients with chronic kidney failure, and the treatment effect has not been clearly proven. This study reports the case of a 56-year-old male diagnosed with chronic kidney failure complaining of uncomfortable leg sensations. The patient underwent Korean medicine treatment using Jakyakgamcho-tang. The IRLS, NRS, and AIS scores were evaluation tools during treatment. This study suggested significantly improved symptoms through the individual interventions of Jakyakgamcho-tang in a restless leg syndrome patient with chronic kidney failure.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.80-80
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• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.1-6
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• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.