• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• BMB Reports
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• v.55 no.6
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• pp.275-280
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• 2022

The treatment of atopic dermatitis (AD) is challenging due to its complex etiology. From epidermal disruption to chronic inflammation, various cells and inflammatory pathways contribute to the progression of AD. As with immunosuppressants, general inhibition of inflammatory pathways can be effective, but this approach is not suitable for long-term treatment due to its side effects. This study aimed to identify a plant extract (PE) with anti-inflammatory effects on multiple cell types involved in AD development and provide relevant mechanistic evidence. Degranulation was measured in RBL-2H3 cells to screen 30 PEs native to South Korea. To investigate the anti-inflammatory effects of Parasenecio auriculatus var. matsumurana Nakai extract (PAE) in AD, production of cytokines and nitric oxide, activation status of FcεRI and TLR4 signaling, cell-cell junction, and cell viability were evaluated using qRT-PCR, western blotting, confocal microscopy, Griess system, and an MTT assay in RBL-2H3, HEK293, RAW264.7, and HaCaT cells. For in vivo experiments, a DNCBinduced AD mouse model was constructed, and hematoxylin and eosin, periodic acid-Schiff, toluidine blue, and F4/80-staining were performed. The chemical constituents of PAE were analyzed by HPLC-MS. By measuring the anti-degranulation effects of 30 PEs in RBL-2H3 cells, we found that Paeonia lactiflora Pall., PA, and Rehmannia glutinosa (Gaertn.) Libosch. ex Steud. show an inhibitory activity of more than 50%. Of these, PAE most dramatically and consistently suppressed cytokine expression, including IL-4, IL-9, IL-13, and TNF-α. PAE potently inhibited FcεRI signaling, which mechanistically supports its basophil-stabilizing effects, and PAE downregulated cytokines and NO production in macrophages via perturbation of toll-like receptor signaling. Moreover, PAE suppressed cytokine production in keratinocytes and upregulated the expression of tight junction molecules ZO-1 and occludin. In a DNCB-induced AD mouse model, the topical application of PAE significantly improved atopic index scores, immune cell infiltration, cytokine expression, abnormal activation of signaling molecules in FcεRI and TLR signaling, and damaged skin structure compared with dexamethasone. The anti-inflammatory effect of PAE was mainly due to integerrimine. Our findings suggest that PAE could potently inhibit multi-inflammatory cells involved in AD development, synergistically block the propagation of inflammatory responses, and thus alleviate AD symptoms.

• The Journal of Advanced Prosthodontics
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• v.14 no.4
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• pp.203-211
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• 2022

PURPOSE. The aim of this study was to introduce rating criteria to evaluate student performance in a newly developed, digital wax-up preclinical program for computer-aided design (CAD) of full-coverage crowns and preliminarily investigate the reliability and internal consistency of the rating system. MATERIALS AND METHODS. This study, conducted in 2017, enrolled 47 fifth-year dental students of Okayama University Dental School. Digital wax-up training included a fundamental practice using computer graphics (CG), multipurpose CAD software programs, and an advanced practice to execute a digital wax-up of the right mandibular second molar (#47). Each student's digital wax-up work (stereolithography data) was evaluated by two instructors using seven qualitative criteria. The total qualitative score (0-90) of the criteria was calculated. The total volumetric discrepancy between each student's digital wax-up work and a reference prepared by an instructor was automatically measured by the CAD software. The inter-rater reliability of each criterion was analyzed using a weighted kappa index. The relationship between the total volume discrepancy and the total qualitative score was analyzed using Spearman's correlation. RESULTS. The weighted kappa values for the seven qualitative criteria ranged from 0.62 - 0.93. The total qualitative score and the total volumetric discrepancy were negatively correlated (ρ = -0.27, P = .09, respectively); however, this was not statistically significant. CONCLUSION. The established qualitative criteria to evaluate students' work showed sufficiently high inter-rater reliability; however, the digitally measured volumetric discrepancy could not sufficiently predict the total qualitative score.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.166-171
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• 2022

Background and Objectives Vocal fold (VF) scar is known to be the most common cause of dysphonia after laryngeal microsurgery (LMS). Steroids reduce postoperative scar formation by inhibiting inflammation and collagen deposition. However, the clinical evidence of whether steroids are helpful in reducing VF scar formation after LMS is still lacking. The purpose of this study is to determine whether intralesional VF steroid injection after LMS helps to reduce postoperative scar formation and voice quality. Materials and Method This study was conducted on 80 patients who underwent LMS for VF polyp, Reinke's edema, and leukoplakia. Among them, 40 patients who underwent VF steroid injection after LMS were set as the injection group, and patients who had similar sex, age, and lesion size and who underwent LMS alone were set as the control group. In each group, stroboscopy, multi-dimensional voice program, Aerophone II, and voice handicap index (VHI) were performed before and 1 month after surgery, and the results were statistically analyzed. Results There were no statistically significant differences in the distribution of sex, age, symptom duration, occupation and smoking status between each group. Both groups consisted of VF polyp (n=21), Reinke's edema (n=11), and leukoplakia (n=9). On stroboscopy, the lesion disappeared after surgery, and the amplitude and mucosal wave were symmetrical on both sides of the VFs in all patients. Acoustic parameters and VHI significantly improved after surgery in all patients. However, there was no significant difference between the injection and control group in most of the results. Conclusion There was no significant difference in the results of stroboscopy, acoustic, aerodynamic, and subjective evaluation before and after surgery in the injection group and the control group.

• Ecology and Resilient Infrastructure
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• v.10 no.3
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• pp.73-84
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• 2023

The objective of this study was to evaluate biodiversity and natural habitat environment of freshwater ecosystem. Our aim was therefore to produce a set of biodiversity and habitat indicators based on multi-parameters of water quality and biodiversity by analyzing the characteristics of the results by indicators. We selected four indicators a) anthropogenic disturbance, b) habitat diversity, c) biodiversity, d) ecosystem structure. The fishes cohabiting with Brachymystax lenok tsinlingensis were Rhynchocypris kumgangensis and Zacco koreanus. As a result of the anthropogenic disturbance evaluation, it was analyzed that vegetation embankment showed a more stable environment in the tributary than the main stream, and other disturbance was not confirmed As a result of the habitat diversity evaluation, it was analyzed that habitat evaluation index showed a high score of 200 more on average, showing an optimal habitat condition. As a result of the biodiversity evaluation, it was analyzed as a clean habitat condition with a high proportion of sensitive species, abundant dissolved oxygen, and little pollutants. As a results of the ecosystem structure, the ecological health condition metrics and appearance of endangered species showed large score deviation, but it was analyzed that the stream ecosystem health was generally excellent. There was a slight correlation between the habitat environment and the results of the nature habitat evaluation according to the appearance of the B. lenok tsinlingensis.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.3
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• pp.375-383
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• 2023

Objectives: Coal-fired power plants feature diverse working conditions, including multi-layered employment structures and irregular work cycles due to outsourcing and non-standardized tasks. The current uniform occupational environment measurement systems have limitations in accurately assessing and evaluating these varied conditions. This study aims to propose alternative measurement and assessment strategies to supplement existing methods. Methods: Major domestic coal-fired power plants were selected as the study targets. To prepare for the study and establish strategies, work processes were identified and existing occupational environment measurement results were compared and analyzed. The study proceeded by employing three strategies: specific exposure groups (SEGs) measurement, continuous monitoring, and supplementary measurements, which were then compared and discussed. Results: Previous exposure index evaluations (5,268 cases) indicated that crystalline silica, a type of respirable particulate matter, had detection limits below the threshold (non-detectable) in 82.6% (4,349 cases) of instances. Exposures below 10% of the exposure limit were observed at a very low concentration of 96.1%. Similar exposure group measurements yielded results where detection limits were below the threshold in 38.2% of cases, and exposures below 10% of the limit were observed in 70.6%. Continuous monitoring indicated detection limits below the threshold in 12.6% of cases, and exposures below 10% of the limit were observed in 75.6%. Instances requiring active workplace management accounted for more than 30% of cases, with SEGs at 11.8% (four cases), showing a higher proportion compared to 3.0% (four cases) in continuous monitoring. For coal dust, exposures below 10% of the limit were highest in legal measurements at 90.2% (113 cases), followed by 74.0% (91 cases) in continuous monitoring, and 47.0% (16 cases) in SEGs. Instances exceeding 30% were most prevalent in SEGs at 14.7% (five cases), followed by legal measurements at 5.0% (eight cases), and continuous monitoring at 2.4% (three cases). When examining exposure levels through arithmetic means, crystalline silica was found to be 104.7% higher in SEGs at 0.0088 mg/m³ compared to 0.0043 mg/m³ in continuous monitoring. Coal dust measurements were highest in SEGs at 0.1247 mg/m³, followed by 0.1224 mg/m³ in legal measurements, and 0.0935 mg/m³ in continuous monitoring. Conclusions: Strategies involving SEGs measurement and continuous monitoring can enhance measurement reliability in environments with irregular work processes and frequent fluctuations in working conditions, as observed in coal-fired power plants. These strategies reduce the likelihood of omitting or underestimating processes and enhance measurement accuracy. In particular, a significant reduction in detection limits below the threshold for crystalline silica was observed. Supplementary measurements can identify worker exposure characteristics, uncover potential risks in blind spots of management, and provide a complementary method for legal measurements.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.673-687
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• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.

• Journal of Industrial Convergence
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• v.21 no.11
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• pp.29-41
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• 2023

In this study, assuming that the maritime manned-unmanned systems, which will be used as the main force of the ROK Navy in the future, conducts its sea line of communication(SLOC) protection operations, the combat effectiveness against major threats was measured, and through this, the development direction of the manned-unmanned systems was suggested. Multi-criteria decision-making techniques such as Delphi and AHP were used to measure combat effectiveness, and the AHP survey was conducted on 40 naval officers, including 25 senior officers who are well-understood in the combat effectiveness of the weapons system and MUM-T. As an evaluation index for measuring combat effectiveness, the OODA loop was set as the main attribute, followed by Observe(0.358), Orient(0.315), Act(0.217), and Decide(0.110). The combat effectiveness of each major threat in SLOC, the lowest alternative, was measured to be 1.68 times higher than the response to maritime conflicts in neighboring countries and 3.61 times higher than the response to transnational threats. These results are expected to support rational decision-making in determining the level of technology required for acquisition of marine manned-unmanned systems and establishing operational plans for naval forces.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.157-172
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• 2024

Autonomous driving technology, when commercialized, has the potential to improve the safety, mobility, and environmental performance of transportation networks. However, safe autonomous driving may be hindered by poor sensor performance and limitations in long-distance detection. Therefore, cooperative autonomous driving that can supplement information collected from surrounding vehicles and infrastructure is essential. In addition, since HDVs, AVs, and CAVs have different ranges of perceivable information and different response protocols, countermeasures are needed for mixed traffic that occur during the transition period of autonomous driving technology. There is a lack of research on traffic flow optimization that considers the penetration rate of autonomous vehicles and the different characteristics of each road segment. The objective of this study is to develop weights based on safety, operational, and environmental factors for each infrastructure control use case and autonomous vehicle MPR. To develop an integrated evaluation index, infra-guidance AHP and hybrid AHP weights were combined. Based on the results of this study, it can be used to give right of way to each vehicle to optimize mixed traffic.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.