• The Journal of Korean Academy of Prosthodontics
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• v.41 no.6
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• pp.720-731
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• 2003

Statement of problem. The importance of fixture design and surface treatment. Purpose. The clinical success of dental in plants is affected by many factors such like as degree of osseointegration, the effective load dispersion for the prostheses, and a lot of attempts have been made to overcome the difficulties. In this study, efforts were made to find the possibility of clinical acceptance of the dental implants of newly designed surface and resorbable blast media surcace. Materials and methods. In this study, two groups of custom-made, screw-shaped implants were prepared. The first with the consisting of Branemark clone design and the other with the new design. These implants were divided into four groups according to the kinds of surface treatment. Four implants($AVANA^{(R)}$, Osstem, Busan, Korea)of each group were installed in twenty rabbits. Group A was consisted of Branemark done implant left as machined, Group B with Branemark clone implants with RBM(Resorbable blast media) surface, Group C with newly designed implants left as machined and Group D with newly designed implants with RBM surface. One of the twenty rabbits died from inflammation and the observation was made for six weeks. Specimens from four groups were observed using scanning electron microscopy with 40, 100, 1000 magnification power and microsurface structures were measured by white-light scanning interferometry for three dimensional surface roughness measurements(Accura $2000^{(R)}$, Intek-Plus, Korea.). Removal torque was measured in 17 rabbits using digital torque gauge(MGT 12R, Mark-10 corp., NY, U.S.A.) immediately after the sacrifice and two rabbits were used for the histologic preparation(EXAKT $310^{(R)}$, Heraeus Kulzer, wehrheim, Germany) of specimens and observed under light microscope. Resonance frequency measurement($Osstell^{(R)}$) was taken with the 19 rabbits at the beginning of the implant fixation and immediately after the sacrifice. Results. Following results were taken from the experiment. 1. The surface of the RBM implants as seen with SEM had rough and irregular pattern with reticular formation compared to that of fumed specimens showing different surface topographies. 2. The newly designed implant with RBM surface had high removal torque value among four groups with no statistical significance. The average removal torque was $49.95{\pm}6.70Ncm$ in Group A, $51.15{\pm}4.40Ncm$ in Group B, $50.78{\pm}9.37Ncm$ in Group C, $51.09{\pm}4.69Ncm$ in Group D. 3. The RFA values were $70.8{\pm}4.3Hz$ in Group A, $71.8{\pm}3.1Hz$ in Group B, $70.9{\pm}2.5Hz$, $72.7{\pm}2.5Hz$ in Group D. Higher values were noted in the groups which had surface treatment compared to the untreated groups with no statistical significance. 4. The results from the histomorphometric evaluation showed a mean percentage of bone-to-implant contact of $45{\pm}0.5%$ in Group A, $55{\pm}3%$ in Group B, $49.5{\pm}0.5%$ in Group C, and $55{\pm}3%$ in Group D. Quite amount of newly formed bone were observed at the surface RBM-treated implants in bone marrow space.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.548-561
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• 2018

The objectives of this study were to construct a three-dimensional (3D) hydrodynamic and water quality model (EFDC) for the river reach between the Daecheong dam and the Sejong weir, which are directly affected by Gap and Miho streams located in the middle of the Geum River, and to evaluate the trophic status and water quality improvement effect according to the flow control and pollutant load reduction scenarios. The EFDC model was calibrated with the field data including waterlevel, temperature and water quality collected from September, 2012 to April, 2013. The model showed a good agreement with the field data and adequately replicated the spatial and temporal variations of water surface elevation, temperature and water quality. Especially, it was confirmed that spatial distributions of nutrients and algae biomass have wide variation of transverse direction. Also, from the analysis of algal growth limiting factor, it was found that phosphorous loadings from Gap and Miho streams to Sejong weir induce eutrophication and algal bloom. The scenario of pollutant load reduction from Gap and Miho streams showed a significant effect on the improvement of water quality; 4.7~18.2% for Chl-a, 5.4~21.9% for TP at Cheongwon-1 site, and 4.2~ 17.3% for Chl-a and 4.7~19.4% for TP at Yeongi site. In addition, the eutrophication index value, identifying the tropic status of the river, was improved. Meanwhile, flow control of Daecheong Dam and Sejong weir showed little effect on the improvement of water quality; 1.5~2.4% for Chl-a, 2.5~ 3.8% for TP at Cheongwon-1 site, and 1.2~2.1% for Chl-a and 0.9~1.5% for TP at Yeongi site. Therefore, improvement of the water quality in Gap and Miho streams is essential and a prerequirement to meet the target water quality level of the study area.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.1-11
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• 2016

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

• (The) Research of the performance art and culture
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• no.22
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• pp.123-155
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• 2011

This study aims to closely read Kim Jong-il's "About Stage Drama Arts" and disclose the new reality as evaluated by him. The study took the method by which to compare Kim Jong-il's theory on drama and North Korea's drama theory in the 1950s, and the findings of this study revealed that it was irrational to grant the adjective "new" to Kim Jong-il's drama theory. This is because tradition inheritance and newness cross each other. First, his tradition inheritance aspect was found in his playwriting method. In playwriting method, Kim Jong-il's argument about characters and language is an extension of the 1950s drama theory, and his theory on JongZa(seeds) is the transformation of the concept proposed in the 1950s. Also, the expression means of dramas and drama arts is dialogue, and his guideline to focus on the art of conversation rather than on acting is interpreted to be a reduced concept of drama arts, compared with the 1950s drama theory. On the other hand, his newness aspect can be clearly discovered in the materialization of stage. The fixed stage background, without dark change, shifts to another situation as it is, and this stage setting is clearly distinguished from the previous stage setting. The attempt is worth highly evaluating to allow the stage to reflect actors' emotional flows and let them act. Also, the attempt is distinctively distinguished from previous drama theories to allow the chorus' positive involvement in dramas so as to directly deliver characters' emotions to the audience and to trigger the audience' response as intended by creators. From the perspectives of drama evaluation, Kim Jong-il's theory and practice regarding stage and music is understood to maximize the audio-visual effects. Therefore, Kim Jeong-il's drama theory, as he argues, is not a completely new theory, but a transformational inheritance of existing drama theories, and a creation theory with focus on expansion of spectacles.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.271-286
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• 2023

The global COVID-19 pandemic has transformed face masks from situational accessories to indispensable items in daily life, prompting a shift in public perception and behavior. While the relaxation of mandatory mask-wearing regulations is underway, a significant number of individuals continue to embrace face masks, turning them into a form of personal expression and identity. This phenomenon has given rise to the Fashion Mask industry, characterized by unique designs and colors, experiencing rapid growth in the market. However, existing research on masks is predominantly focused on their efficacy in preventing infection or exploring attitudes during the pandemic, leaving a gap in understanding consumer preferences for mask design. We address this gap by investigating consumer perceptions and preferences for two prevalent mask designs-horizontal fold flat masks and vertical fold flat masks. Through a comprehensive approach involving surveys and eye-tracking experiments, we aim to unravel the subtle differences in how consumers perceive these designs. Our research questions focus on determining which design is more appealing and exploring the reasons behind any observed differences. The study's findings reveal a clear preference for vertical fold flat masks, which are not only preferred but also perceived as unique, sophisticated, three-dimensional, and lively. The eye-tracking analysis provides insights into the visual attention patterns associated with mask designs, highlighting the pivotal role of the fold line in influencing these patterns. This research contributes to the evolving understanding of masks as a fashion statement and provides valuable insights for manufacturers and marketers in the Fashion Mask industry. The results have implications beyond the pandemic, emphasizing the importance of design elements in sustaining consumer interest in face masks.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.1
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• pp.29-35
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• 2014

Purpose : This study has already started commercial Gated RapidArc automation equipment which was not previously in the Gated radiation therapy can be performed simultaneously with the VMAT Gated RapidArc radiation therapy to the accuracy of the analysis to evaluate the usability, Amplitude mode applied to the patient. Materials and Methods : The analysis of the distribution of radiation dose equivalent quality solid water phantom and GafChromic film was used Film QA film analysis program using the Gamma factor (3%, 3 mm). Three-dimensional dose distribution in order to check the accuracy of Matrixx dosimetry equipment and Compass was used for dose analysis program. Periodic breathing synchronized with solid phantom signals Phantom 4D Phantom and Varian RPM was created by breathing synchronized system, free breathing and breath holding at each of the dose distribution was analyzed. In order to apply to four patients from February 2013 to August 2013 with liver cancer targets enough to get a picture of 4DCT respiratory cycle and then patients are pratice to meet patient's breathing cycle phase mode using the patient eye goggles to see the pattern of the respiratory cycle to be able to follow exactly in a while 4DCT images were acquired. Gated RapidArc treatment Amplitude mode in order to create the breathing cycle breathing performed three times, and then at intervals of 40% to 60% 5-6 seconds and breathing exercises that can not stand (Fig. 5), 40% While they are treated 60% in the interval Beam On hold your breath when you press the button in a way that was treated with semi-automatic. Results : Non-respiratory and respiratory rotational intensity modulated radiation therapy technique absolute calculation dose of using computerized treatment plan were shown a difference of less than 1%, the difference between treatment technique was also less than 1%. Gamma (3%, 3 mm) and showed 99% agreement, each organ-specific dose difference were generally greater than 95% agreement. The rotational intensity modulated radiation therapy, respiratory synchronized to the respiratory cycle created Amplitude mode and the actual patient's breathing cycle could be seen that a good agreement. Conclusion : When you are treated Non-respiratory and respiratory method between volumetric intensity modulated radiation therapy rotation of the absolute dose and dose distribution showed a very good agreement. This breathing technique tuning volumetric intensity modulated radiation therapy using a rotary moving along the thoracic or abdominal breathing can be applied to the treatment of tumors is considered. The actual treatment of patients through the goggles of the respiratory cycle to create Amplitude mode Gated RapidArc treatment equipment that does not automatically apply to the results about 5-6 seconds stopped breathing in breathing synchronized rotary volumetric intensity modulated radiation therapy facilitate could see complement.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.