• Journal of Periodontal and Implant Science
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• v.50 no.6
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• pp.358-367
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• 2020

Purpose: The aim of this study was to investigate the efficacy and validity of subgingival bacterial sampling using a retraction cord, and to evaluate how well this sampling method reflected changes in periodontal conditions after periodontal therapy. Methods: Based on clinical examinations, 87 subjects were divided into a healthy group (n=40) and a periodontitis group (n=47). Clinical measurements were obtained from all subjects including periodontal probing depth (PD), bleeding on probing (BOP), clinical attachment loss (CAL), and the plaque index. Saliva and gingival crevicular fluid (GCF) as a subgingival bacterial sample were sampled before and 3 months after periodontal therapy. The salivary and subgingival bacterial samples were analyzed by reverse-transcription polymerase chain reaction to quantify the following 11 periodontal pathogens: Aggregatibacter actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), Tannerella forsythus (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Pavimonas micra (Pm), Campylobacter rectus (Cr), Prevotella nigrescens (Pn), Eikenella corrodens (Ec), and Eubacterium nodatum (En). Results: Non-surgical periodontal therapy resulted in significant decreases in PD (P<0.01), CAL (P<0.01), and BOP (P<0.05) after 3 months. Four species (Pg, Tf, Pi, and Pm) were significantly more abundant in both types of samples in the periodontitis group than in the healthy group. After periodontal therapy, Cr was the only bacterium that showed a statistically significant decrease in saliva, whereas statistically significant decreases in Cr, Pg, and Pn were found in GCF. Conclusions: Salivary and subgingival bacterial sampling with a gingival retraction cord were found to be equivalent in terms of their accuracy for differentiating periodontitis, but GCF reflected changes in bacterial abundance after periodontal therapy more sensitively than saliva.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.53-63
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• 2019

To provide a hand gesture interface through deep learning in mobile environments, research on the light-weighting of networks is essential for high recognition rates while at the same time preventing degradation of execution speed. This paper proposes a method of real-time recognition of written characters in the air using a finger on mobile devices through the light-weighting of deep-learning model. Based on the SSD (Single Shot Detector), which is an object detection model that utilizes MobileNet as a feature extractor, it detects index finger and generates a result text image by following fingertip path. Then, the image is sent to the server to recognize the characters based on the learned OCR model. To verify our method, 12 users tested 1,000 words using a GALAXY S10+ and recognized their finger with an average accuracy of 88.6%, indicating that recognized text was printed within 124 ms and could be used in real-time. Results of this research can be used to send simple text messages, memos, and air signatures using a finger in mobile environments.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4122-4129
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• 2021

The counting efficiencies obtained using anthropomorphic physical phantoms are generally used in whole-body counting measurements to determine the level of internal contamination in the body. Geometrical discrepancies between phantoms and measured individuals affect the counting efficiency, and thus, considering individual physical characteristics is crucial to improve the accuracy of activity estimates. In the present study, the counting efficiencies of whole-body counting measurements were calculated considering individual physical characteristics by employing Monte Carlo simulation for calibration. The NaI(Tl)-based stand-up and HPGe-based bed type commercial whole-body counters were used for calculating the counting efficiencies. The counting efficiencies were obtained from 19 computational phantoms representing various shapes and sizes of the measured individuals. The discrepancies in the counting efficiencies obtained using the computational and physical phantoms range from 2% to 33%, and the results indicate that the counting efficiency depends on the size of the measured individual. Taking into account the body size, the equations for estimating the counting efficiencies were derived from the relationship between the counting efficiencies and the body-build index of the subject. These equations can aid in minimizing the size dependency of the counting efficiency and provide more accurate measurements of internal contamination in whole-body counting measurements.

• Journal of Oral Medicine and Pain
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• v.44 no.2
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• pp.54-58
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• 2019

Purpose: The aim of this study is to evaluate the correlation between tongue pain and tenderness of tongue by digital palpation (DP) in Burning Mouth Syndrome (BMS) patients. Methods: One hundred thirty-four consecutive patients (60 BMS with tongue pain and 74 non-BMS) who attended the Department of Oral Medicine (Chosun University Dental Hospital, Gwangju, Korea) from January 2018 to December 2018 were included in this study. The examined sites were anterior, lateral (right and left) and central part of the tongue. The pain sites were reported by the patients and the tender points on DP test were recorded by the clinicians. DP test was performed by well-trained clinicians with palpation of the tongue with 0.5 kg pressure using the thumb and index finger. Obtained results for BMS and non-BMS group were compared using t-test (p<0.05). Results: 1. The BMS group had higher tender score on DP test of the tongue and there was a significant difference between the BMS and non-BMS groups. 2. The accuracy of the pain site and the tender point was shown to be 0.68 total (anterior 0.68; right lateral 0.69; left lateral 0.70; central 0.61). Conclusions: This study suggests that the tenderness to DP of the tongue could be related to the painsites in the BMS patients. Further study is needed to confirm the usefulness of DP test of the tongue to examine the BMS patients.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.4
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• pp.1-10
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• 2019

Unmanned aerial vehicle(UAV) can acquire images with lower cost than conventional manned aircraft and commercial satellites. It has the advantage of acquiring high-resolution aerial images covering in the field area more than 50 ha. The purposes of this study is to develop the rice grain yield distribution using UAV. In order to develop a technology for estimating the rice yield using UAV images, time series UAV aerial images were taken at the paddy fields and the data were compared with the rice yield of the harvesting area for two rice varieties(Singdongjin, Dongjinchal). Correlations between the vegetation indices and rice yield were ranged from 0.8 to 0.95 in booting period. Accordingly, rice yield was estimated using UAV-derived vegetation indices($R^2=0.70$ in Sindongjin, $R^2=0.92$ in Donjinchal). It means that the rice yield estimation using UAV imagery can provide less cost and higher accuracy than other methods using combine with yield monitoring system and satellite imagery. In the future, it will be necessary to study a variety of information convergence and integration systems such as image, weather, and soil for efficient use of these information, along with research on preparing management practice work standards such as pest control and nutrient use based on UAV image information.

• Structural Engineering and Mechanics
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• v.71 no.2
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• pp.185-196
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• 2019

In this investigation, study of the static and dynamic behaviors of functionally graded beams (FGB) is presented using a hyperbolic shear deformation theory (HySDT). The simply supported FG-beam is resting on the elastic foundation (Winkler-Pasternak types). The properties of the FG-beam vary according to exponential (E-FGB) and power-law (P-FGB) distributions. The governing equations are determined via Hamilton's principle and solved by using Navier's method. To show the accuracy of this model (HySDT), the current results are compared with those available in the literature. Also, various numerical results are discussed to show the influence of the variation of the volume fraction of the materials, the power index, the slenderness ratio and the effect of Winkler spring constant on the fundamental frequency, center deflection, normal and shear stress of FG-beam.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.251-256
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• 2019

In this paper we proposed system and algorithm to recognize hand gestures based on the millimeter wave that is in 65GHz bandwidth. The proposed system is composed of millimeter wave radar board, analog to data conversion and data capture board and notebook to perform gesture recognition algorithms. As feature vectors in proposed algorithm. we used global and local zernike moment descriptor which are robust to distort by rotation of scaling of 2D data. As Experimental result, performance of the proposed algorithm is evaluated and compared with those of algorithms using single global or local zernike descriptor as feature vectors. In analysis of confusion matrix of algorithms, the proposed algorithm shows the better performance in comparison of precision, accuracy and sensitivity, subsequently total performance index of our method is 95.6% comparing with another two mehods in 88.4% and 84%.

• Applied Chemistry for Engineering
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• v.30 no.1
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• pp.29-33
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• 2019

Carbon nanotubes, nanofibers and powders were used for acetylene adsorption experiments. A total of 15 different experiments were designed by 3-level of Box-Behnken Design (BBD) with 3 factors including the Pd concentration of 0 to 5%, adsorption temperature of 30 to $80^{\circ}C$ and $C_2H_2/CO_2$ of 3 to 10. Based on those data, a second order polynomial regression analysis was used to derive the adsorption amount prediction equation according to operating conditions. The adsorption temperature showed the greatest influence index while the $C_2H_2/CO_2$ ratio showed the smallest according to the F-value measurement of the ANOVA analysis. However, there was little interaction between major factors. In the adsorption optimization analysis, a 22.0 mmol/g was adsorbed under the conditions of Pd concentration of 3.0%, adsorption temperature of $47^{\circ}C$ and $C_2H_2/CO_2$ of 10 with 95.9% accuracy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Coupled systems mechanics
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• v.8 no.4
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• pp.315-338
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• 2019

The numerical investigations have been carried out on reinforced concrete slab against blast loading to demonstrate the accuracy and effectiveness of the finite element based numerical models using commercial package ABAQUS. The response of reinforced concrete slab have been studied against the influence of weight of TNT, standoff distance, boundary conditions, influence of air blast and surface blast. The results thus obtained from simulations were compared with the experiments available in literature. The inelastic behavior of concrete and steel reinforcement bar has been incorporated through concrete damage plasticity model and Johnson-cook models available in ABAQUS were presented. The predicted results through numerical simulations of the present study were found in close agreement with the experimental results. The damage mechanism and stress response of target were assessed based on the intensity of deformations, impulse velocity, von-Mises stresses and damage index in concrete. The results indicate that the standoff distance has great influence on the survivability of RC slab against blast loading. It is concluded that the velocity of impulse wave was found to be decreased from 17 to 11 m/s when the mass of TNT is reduced from 12 to 6 kg. It is observed that the maximum stress in the concrete was found to be in the range of 15 to $20N/mm^2$ and is almost constant for given charge weight. The slab with two short edge discontinuous end condition was found better and it may be utilised in designing important structures. Also it is observed that the deflection in slab by air blast was found decreased by 60% as compared to surface blast.