• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.157-166
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• 2022

Exercise is necessary for a healthy life, but it is recommended that it be conducted in a non-face-to-face environment in the context of an epidemic such as COVID-19. However, in the existing non-face-to-face exercise content, it is possible to recognize exercise movements, but the process of interpreting and providing feedback information is not automated. Therefore, in this paper, to solve this problem, we propose a method of creating a formalized rule to track the contents of exercise and the motions that constitute it. To make such a rule, first make a rule for the overall exercise content, and then create a tracking rule for the motions that make up the exercise. A motion tracking rule can be created by dividing the motion into steps and defining a key frame pose that divides the steps, and creating a transition rule between states and states represented by the key frame poses. The rules created in this way are premised on the use of posture and motion recognition technology using motion capture equipment, and are used for logical development for automation of application of these technologies. By using the rules proposed in this paper, not only recognizing the motions appearing in the exercise process, but also automating the interpretation of the entire motion process, making it possible to produce more advanced contents such as an artificial intelligence training system. Accordingly, the quality of feedback on the exercise process can be improved.

• Journal of Korean Medicine Rehabilitation
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• v.32 no.2
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• pp.19-36
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• 2022

Objectives This study is to investigate the effects and mechanisms of Imyo-san (IMS) on the obese mice model induced by high-fat diet. Methods Antioxidative capacity was measured by in vitro method. C57BL/6 mice were randomly assigned into 5 groups (n=7). Normal group was fed general diet (Normal). The other 4 groups were fed high fat diet (HFD) with water (Control), with Garcinia gummi-gutta (GG, Garcinia gummi-gutta 200 mg/kg), with low-dose IMS (IMSL, Imyo-san 0.54 g/kg) and with high-dose IMS (IMSH, Imyo-san 1.08 g/kg). Results IMS showed high radical scavenging activity. After 6 week experiment, body weight, food intake, food efficiency ratio (FER), epididymal fat and liver weight, triglyceride (TG), total cholesterol (TC), high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, very low density lipoprotein (VLDL) cholesterol, sterol regulatory element-binding protein-1 (SREBP-1), phospho-acetyl-CoA carboxylase (p-ACC), fatty acid synthase (FAS), stearoyl-CoA desaturase-1 (SCD-1), SREBP-2, 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), phospho-liver kinase B1 (p-LKB1), phospho-AMP-activated protein kinase (p-AMPK), peroxisome proliferator-activated receptor 𝛼 (PPAR𝛼), peroxisome proliferator-activated receptor 𝛾 coactivator-1𝛼 (PGC-1𝛼), uncoupling protein-2 (UCP-2), carnitine palmitoyltransferase 1A (CPT-1A), and histology of liver and epididymal fat were measured and analysed. Body weight gain, FER, liver and epididymal fat weight of IMS groups were significantly decreased. There were significant improvements in blood lipids with less TG, TC, LDL-cholesterol, VLDL-cholesterol and more HDL-cholesterol. Proteins associated with lipid synthesis (SREBP-1, p-ACC, FAS, SCD-1) and cholesterol (SREBP-2, HMGCR) was improved. Factors regulating lipid synthesis and lipid catabolism (p-LKBI, p-AMPK, PPARα, PGC-1α, UCP-2, CPT-1A) were increased. In histological examinations, IMS group had smaller fat droplets than control group. All results increased depending on concentration. Conclusions It can be suggested that IMS has anti-obesity effects with improving lipid metabolism.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

• Journal of Wetlands Research
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• v.24 no.3
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• pp.170-184
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• 2022

The Riparian Buffer Zone(RBZ) is a sustainable social-ecological system created in the middle zone between water and land. For the RBZ, close communication with the local community is important, and it is necessary to promote it as a communicative environmental planning process. In this study, for the RBZ project, three strategies are presented as a communicative act to understand and implement planning. First, government-led projects were avoided and improved to a process in which citizens and stakeholders participated together, centered on local partnership. Second, it was intended to introduce design criterias in terms of enhancing the function of ecosystem services that citizens can sympathize with, and to increase acceptance and awareness through the planning of preferred spaces and facilities. Third, after a balanced plan for habitats, water cycle-based ecological environment, ecological experience and open space, citizens felt the restoration effect and value as an ecological resources, and a system was prepared to participate in the operation and management. This study will work as a process model based on citizens's participation. In addition, it will be possible to provide lessons for the change of the policy paradigm for the RBZ and the implementation of similar projects in the future.

• The Korean Journal of Physiology and Pharmacology
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• v.26 no.5
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• pp.335-345
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• 2022

Pyroptosis is an inflammatory form of programmed cell death that is linked with invading intracellular pathogens. Cardiac pyroptosis has a significant role in coronary microembolization (CME), thus causing myocardial injury. Tanshinone IIA (Tan IIA) has powerful cardioprotective effects. Hence, this study aimed to identify the effect of Tan IIA on CME and its underlying mechanism. Forty Sprague-Dawley (SD) rats were randomly grouped into sham, CME, CME + low-dose Tan IIA, and CME + high-dose Tan IIA groups. Except for the sham group, polyethylene microspheres (42 ㎛) were injected to establish the CME model. The Tan-L and Tan-H groups received intraperitoneal Tan IIA for 7 days before CME. After CME, cardiac function, myocardial histopathology, and serum myocardial injury markers were assessed. The expression of pyroptosis-associated molecules and TLR4/MyD88/NF-κB/NLRP3 cascade was evaluated by qRT-PCR, Western blotting, ELISA, and IHC. Relative to the sham group, CME group's cardiac functions were significantly reduced, with a high level of serum myocardial injury markers, and microinfarct area. Also, the levels of caspase-1 p20, GSDMD-N, IL-18, IL-1β, TLR4, MyD88, p-NF-κB p65, NLRP3, and ASC expression were increased. Relative to the CME group, the Tan-H and Tan-L groups had considerably improved cardiac functions, with a considerably low level of serum myocardial injury markers and microinfarct area. Tan IIA can reduce the levels of pyroptosis-associated mRNA and protein, which may be caused by inhibiting TLR4/MyD88/NF-κB/NLRP3 cascade. In conclusion, Tanshinone IIA can suppress cardiomyocyte pyroptosis probably through modulating the TLR4/MyD88/NF-κB/NLRP3 cascade, lowering cardiac dysfunction, and myocardial damage.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.45-52
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• 2023

The power generation method using expensive diesel has operation problems such as high cost diesel generator and a lack of reserved power due to increase of power demand in some islands, requiring expansion of power generation facilities. To solve this problems, it is necessary to improve the efficiency of power generation facilities through an ORC(Organic Rankin Cycle) system application that uses waste heat as a heat source. Therefore, localized application technology of price competitive and highly reliable ORC power generation system is needed, and optimization technology of generators is having great effect, so this study performed two generator designs to get a high-efficiency generator with an optimized 30kW output. The comparison of simulation data for two designed models showed that a generator with SPM factor of 46.2% had an efficiency of 92.1% and a power ouput of about 23.2kW based on 12,000rpm, a generator with SPM factor of 44.46%, had a power output of 27.9kW and efficiency of 93.6% based on above rpm. For the verification of improved design model with SPM factor of 44.46%, the prototype test system with 110kW motor dynamometer was installed and got to the efficiency of 92.08% with conditions of the rated capacity 25kW at 12,000rpm, the test results of prototype generator showed the validity of generator design.

• Microbiology and Biotechnology Letters
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• v.50 no.4
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• pp.574-583
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• 2022

Ficus vasculosa Wall. ex Miq. (FV) has been used as a herbal medicine in Southeast Asia and its antioxidant activity has been shown in previous studies. However, it has not yet been elucidated whether FV exerts anti-inflammatory effects on activated-macrophages. Thus, we aimed to evaluate the ameliorative property of FV methanol extract (FM) on lipopolysaccharide (LPS)-induced inflammatory responses and the underlying molecular mechanisms in RAW264.7 macrophages. The experimental results indicated that FM decreased the production of inflammatory mediators (NO/PGE2) and the mRNA/protein expression of iNOS and COX-2 in LPS-stimulated RAW264.7 cells. FM also reduced the secretion of interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α and monocyte chemoattractant protein (MCP)-1 in LPS-stimulated RAW264.7 cells. Results also demonstrated that FM improved inflammatory response in LPS-stimulated A549 airway epithelial cells by inhibiting the production of cytokines, such as IL-1β, IL-6 and TNF-α. In addition, FM suppressed MAPK activation and NF-κB nuclear translocation induced by LPS. FM also upregulated the mRNA/protein expression levels of heme oxygenase-1 and the nuclear translocation of nuclear factor erythroid 2-related factor 2 in RAW264.7 cells. In an experimental animal model of LPS-induced acute lung injury, the increased levels of molecules in bronchoalveolar lavage (BAL) fluid were suppressed by FM administration. Collectively, it was founded that FM has anti-inflammatory properties on activated-macrophages by suppressing inflammatory molecules and regulating the activation of MAPK/NF-κB signaling.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.