• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.576-584
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• 2015

This research studiedthe correlation between dental implant patients' oral hygiene behaviors and satisfaction. By doing so, this study seeks to provide the basic data for the development of diagonal and educational programs to improve implant prosthesis maintenance and satisfaction. The data was collected from January 7 to June 30, 2014, analyzed by SAS (ver 9.2) and for this research, 6 dental hospitals and clinics in Daegu, Busan and Jinju, South Korea were examined for their implant patients receiving treatment. As a result, a total of 266 sets of data were investigated herein to reach the following findings: 1. The survey on status of dental implant prosthesis use and maintenance knowledge found 1.41 on average out of the total score of 3. 2. In the survey on implant prosthesis maintenance after putting in, the dental scaling cycle was found to be 'none' (63.2%). 3. In the survey on implant use status and satisfaction with their implant prosthesis, the masticatory function (p=0.001) were found to be significantly higher in the group using such oral-hygiene goods. 4. In the correlation analysis among one's knowledge on dental implant maintenance, discomfort and satisfaction, it was found that the more they used oral-hygiene supplementary goods (${\beta}=0.095$), the more they had knowledge on implant maintenance (${\beta}=0.069$) and the more they experienced oral health education (${\beta}=0.032$), the higher their satisfaction levels were. It is deemed that, for enhanced satisfaction of dental implant patients, their maintenance behaviors need to be further improved through oral health educational programs.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.449-460
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• 2020

Super-resolution is a technique used to reconstruct an image with low-resolution into that of high-resolution. Recently, deep-learning based super resolution has become the mainstream, and applications of these methods are widely used in the remote sensing field. In this paper, we propose a super-resolution method based on the deep back-projection network model to improve the satellite image resolution by the factor of four. In the process, we customized the loss function with the edge loss to result in a more detailed feature of the boundary of each object and to improve the stability of the model training using generative adversarial network based on Wasserstein distance loss. Also, we have applied the detail preserving image down-scaling method to enhance the naturalness of the training output. Finally, by including the modified-residual learning with a panchromatic feature in the final step of the training process. Our proposed method is able to reconstruct fine features and high frequency information. Comparing the results of our method with that of the others, we propose that the super-resolution method improves the sharpness and the clarity of WorldView-3 and KOMPSAT-2 images.

• Journal of Dental Rehabilitation and Applied Science
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• v.22 no.2
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• pp.173-180
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• 2006

Objectives The aim of this study is to compare the quality of root canal preparation completed using MFile-$System^{(R)}$ instrument ( Komet, Gebr.Brsaseler, Germany) and conventional stainless steel file in the canals of Maxillary molar teeth that had a canal curvature between $25^{\circ}$ or more Materials & Methods Buccal canals of 24 first and second maxillary molar teeth, extracted for periodontal and prosthetic reasons were used. Tissue fragments and calcified debris were removed from teeth by scaling and the teeth were stored in 10% formalin solution for 24 hour. Then, teeth were stored in saline until used. To be included the roots had to have completed formed apices and angle of curvature ranging between $25^{\circ}$ or more according to the criteria described by Schneider(1971). Palatal and Second mesiobuccal canals were not included. Teeth were embedded into transparent acrylic. The teeth were randomly divided into two experimental groups. All teeth were scanned by Dental CT (PSR9000N, Asahi, Japan) to determine the root canal shape before instrumentation. Image slices were prepared from the apical end point to the pulp chamber. The first two sections were 2 mm from the apical end of root and 2 mm below the orifice. Further section was recorded, dividing the distance between the sections of apical and coronal levels into two equal lengths. 12 teeth were instrumented using stainless steel fileand another 12 teeth were instrumented using MFile-$System^{(R)}$. Following the completion of the instrumentation, the teeth were again scanned and compared with the cross sectional images taken prior to canal preparation. Amount of transportation and centering ability was assessed. Student's t-test was used for statistical analysis. Result Less transportation occurred with MFile-$System^{(R)}$ rotary instrumentation than stainless steel instrument. MFile-$System^{(R)}$ had better centering ability than stainless steel instrument. Conclusion MFile-$System^{(R)}$ rotary instrumentation transported canals less and had good centering ability.

• Korean journal of food and cookery science
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• v.3 no.1
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• pp.71-77
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• 1987

This study was to investigate the synergistic taste effect between monosodium glutamate(MSG) and 5'-ribonucleotides consisted of disodium 5'-inosinate (IMP) and 5'-guanylate (GMP) as 1:1 ratio. Solvent was distilled water. Sensory evaluation with 10 panelists was performed by using ratio scaling method (magnitude estimation). The results were as follows: 1) Taste intensities were increased, as nucleotides content to MSG increased. 2) Multiple regression analyses were carried out with the taste intensity data as a function of nucleotides content at three concentrations of seasonings, 0.025%, 0.05% and 0.1%. 3) Predicted taste intensities ($Y_P$) were calculated from the regression equation. Also taste intensity ratios ($Y_{TR}$)-$Y_{TR}$=$Y_P$/taste intensity of MSG only-were calculated. 4) The taste intensity ratios ($Y_{TR}$) at three concentrations of seasonings in the same nucleotides contents showed about the same. Therefore, instead of above regression equations, only one multiple regression equation expressing $Y_P$ of nucleotides seasonings could be determined, as functions of nucleotides content and seasoning concentration.

• International Journal of Highway Engineering
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• v.11 no.3
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• pp.51-60
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• 2009

This study investigates the fresh state characteristics, strength, chloride ion penetration resistance and freeze-thaw resistance of the suggested high strength-high durability cement concrete pavement. The required workability and air content could be achieved by using an appropriate admixtures. However its dosage should be carefully determined through field trial batches. Compressive strength increased with the increased cement content and, in particular, high cement volume concrete continuously developed strength up to 90 days. No clear relationship, however, existed between flexural strength and cement content. Chloride penetration resistance seemed as a function of curing age rather than the cement content. Freeze-thaw resistance test was conducted using two different coolants, tap water and 4% NaCl solution. When the tap water was used no severe damage was observed up to 300 cycles regardless the air content. Under 4% NaCl solution, specimens of 326kg/$m^3$ cement content showed severe damage with surface scaling. Based on the experimental investigations herein, it is highly recommended that the cement content be greater than 400kg/$m^3$ for strength-high durability cement concrete pavement structures.

• Journal of Dental Rehabilitation and Applied Science
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• v.30 no.2
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• pp.131-137
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• 2014

Purpose: This study examined the efficiency and wear of periodontal instrument sharpening using aluminum oxide stones with different levels of roughness. Materials and Methods: Thirty new No. 9-10 Gracey curets were used in this study. All curets had become dull after scaling and root planing. After similar blunting, the instruments were divided randomly into three groups (240, 600, 800 grit) containing 10 curets each. The stones were applied correctly to the lateral surface of each curet to maintain the $70-80^{\circ}$ angle. After resharpening, sharpness of the curets was examined by an optical microscope. After 20, 40, 60, and 80 strokes, the wear was measured at 1 mm and 2 mm from the tip of the cutting edge using a digital caliper. The data was analyzed statistically using analysis of variance (ANOVA) with repeated measures, 2-way ANOVA, and a Fisher's exact test. Results: The degree of sharpness increased significantly (P < 0.001) as the number of sharpening strokes grew for all stones. A comparison of the degree of sharpness on the same number of strokes showed that the 240 grit group significantly excelled the other groups on 5 and 10 strokes, respectively (P < 0.001). The mean wear showed no statistically significant difference among the groups (P > 0.05). Conclusion: The efficiency of Gracey curet resharpening was enhanced with more coarse stones, though we should consider the wear of the instrument during resharpening.

• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.51-56
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• 2008

The waveform inversion for isotropic media has ever been studied since the 1980s, but there has been few studies for anisotropic media. We present a seismic waveform inversion algorithm for 2-D heterogeneous transversely isotropic structures. A cell-based finite difference algorithm for anisotropic media in time domain is adopted. The steepest descent during the non-linear iterative inversion approach is obtained by backpropagating residual errors using a reverse time migration technique. For scaling the gradient of a misfit function, we use the pseudo Hessian matrix which is assumed to neglect the zero-lag auto-correlation terms of impulse responses in the approximate Hessian matrix of the Gauss-Newton method. We demonstrate the use of these waveform inversion algorithm by applying them to a two layer model and the anisotropic Marmousi model data. With numerical examples, we show that it's difficult to converge to the true model when we assumed that anisotropic media are isotropic. Therefore, it is expected that our waveform inversion algorithm for anisotropic media is adequate to interpret real seismic exploration data.

• Smart Media Journal
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• v.8 no.2
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• pp.58-65
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• 2019

Automatic text summarization is a process of shortening a text document by either extraction or abstraction. The abstraction approach inspired by deep learning methods scaling to a large amount of document is applied in recent work. Abstractive text summarization involves utilizing pre-generated word embedding information. Low-frequent but salient words such as terminologies are seldom included to dictionaries, that are so called, out-of-vocabulary(OOV) problems. OOV deteriorates the performance of Encoder-Decoder model in neural network. In order to address OOV words in abstractive text summarization, we propose a copy mechanism to facilitate copying new words in the target document and generating summary sentences. Different from the previous studies, the proposed approach combines accurate pointing information and selective copy mechanism based on bidirectional RNN and bidirectional LSTM. In addition, neural network gate model to estimate the generation probability and the loss function to optimize the entire abstraction model has been applied. The dataset has been constructed from the collection of abstractions and titles of journal articles. Experimental results demonstrate that both ROUGE-1 (based on word recall) and ROUGE-L (employed longest common subsequence) of the proposed Encoding-Decoding model have been improved to 47.01 and 29.55, respectively.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2014.10a
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• pp.25-48
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• 2014

Increased frequency of climate extremes is another face of climate change confronted by humans, resulting in catastrophic losses in agriculture. While climate extremes take place on many scales, impacts are experienced locally and mitigation tools are a function of local conditions. To address this, agrometeorological early warning systems must be place and location based, incorporating the climate, crop and land attributes at the appropriate scale. Existing services often lack site-specific information on adverse weather and countermeasures relevant to farming activities. Warnings on chronic long term effects of adverse weather or combined effects of two or more weather elements are seldom provided, either. This lecture discusses a field-specific early warning system implemented on a catchment scale agrometeorological service, by which volunteer farmers are provided with face-to-face disaster warnings along with relevant countermeasures. The products are based on core techniques such as scaling down of weather information to a field level and the crop specific risk assessment. Likelihood of a disaster is evaluated by the relative position of current risk on the standardized normal distribution from climatological normal year prepared for 840 catchments in South Korea. A validation study has begun with a 4-year plan for implementing an operational service in Seomjin River Basin, which accommodates over 60,000 farms and orchards. Diverse experiences obtained through this study will certainly be useful in planning and developing the nation-wide disaster early warning system for agricultural sector.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.