• Journal of the Korean Crystal Growth and Crystal Technology
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• v.31 no.1
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• pp.32-36
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• 2021

This study was investigated in carbon diffusion behavior of laser-carburized TiZrN coating layer and the changes of mechanical properties. The carbon paste was deposited on TiZrN coatings, and the laser was irradiated to carburize into the coatings. The XRD peak corresponding to the (111) plane shifted to a lower angle after the carburization, showing the lattice expansion by doped carbon. The decreased grain size implied the compression by the grain boundary diffusion of carbon. The XPS spectra for the bonding states of carbon was analyzed that carbon was substitute to nitrogen atoms in TiZrN, as carbide, through the thermal energy of laser. In addition, the combination of sp2 and sp3 hybridized bonds represented the formation of an amorphous carbon. The cross-sectional TEM image and the inverse FFT of the TiZrN coating after carburizing were observed as the wavy shape, confirming the amorphous phase located in grain boundaries. After the carburization, the hardness increased from 34.57 GPa to 38.24 GPa, and the friction coefficient decreased by 83 %. In particular, the ratio of hardness and elastic modulus (H/E) which is used as an index of the elastic recovery, increased from 0.11 to 0.15 and the wear rate improved by 65 %.

• Journal of Dental Anesthesia and Pain Medicine
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• v.23 no.1
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• pp.29-37
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• 2023

Background: A high incidence (40-73%) of postoperative nausea and vomiting (PONV) has been reported following orthognathic surgery, and various risk factors have been associated with it. Identifying PONV risk factors based on initial onset time will help establish preventive measures. This study aimed to identify factors that are significantly related to PONV based on the initial onset time after orthognathic surgery. Methods: This study included 590 patients who underwent orthognathic surgery. Multivariate logistic regression analysis was performed to identify the risk factors that are significantly related to PONV. The objective variables were classified into three categories: no PONV, early PONV (initial onset time: 0-2 h after anesthesia), and late PONV (initial onset time: 2-24 h after anesthesia). The explanatory variables included relevant risk factors for PONV, as considered in previous studies. Results: Total intravenous anesthesia with propofol was a significant depressant factor for early PONV (adjusted odds ratio [aOR] = 0.340, 95% confidence interval [CI] = 0.209-0.555) and late PONV (aOR = 0.535, 95% CI = 0.352-0.814). The administration of a combination of intraoperative antiemetics (vs. no administration) significantly reduced the risk of early PONV (aOR = 0.464, 95% CI = 0.230-0.961). Female sex and young age were significant risk factors for late PONV (aOR = 1.492, 95% CI = 1.170-1.925 and unit aOR = 1.033, 95% CI = 1.010-1.057, respectively). Conclusion: We identified factors that are significantly related to PONV based on the initial onset time after orthognathic surgery. Total intravenous anesthesia with propofol significantly reduced the risk of PONV not only in the early period (0-2 h after anesthesia) but also in the late period (2-24 h after anesthesia).

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.363-373
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• 2023

Forest biomass surveys are regularly conducted to assess and manage forests as carbon sinks. LiDAR (Light Detection and Ranging), a remote sensing technology, has attracted considerable attention, as it allows for objective acquisition of forest structure information with minimal labor. In this study, we propose a method for estimating overstory and understory biomass in forest stands using backpack laser scanning (BPLS) and unmanned aerial vehicle laser scanning (UAV-LS), and assessed its accuracy. For overstory biomass, we analyzed the accuracy of BPLS and UAV-LS in estimating diameter at breast height (DBH) and tree height. For understory biomass, we developed a multiple regression model for estimating understory biomass using the best combination of vertical structure metrics extracted from the BPLS data. The results indicated that BPLS provided accurate estimations of DBH (R² =0.92), but underestimated tree height (R² =0.63, bias=-5.56 m), whereas UAV-LS showed strong performance in estimating tree height (R² =0.91). For understory biomass, metrics representing the mean height of the points and the point density of the fourth layer were selected to develop the model. The cross-validation result of the understory biomass estimation model showed a coefficient of determination of 0.68. The study findings suggest that the proposed overstory and understory biomass survey methods using BPLS and UAV-LS can effectively replace traditional biomass survey methods.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.581-589
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• 2021

Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.279-289
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• 2022

Since the frequency of heavy rainfall is increasing due to climate change, water levels in the river exceed past historical records. The rating-curve is to convert water level into flow dicscharge from the regression analysis of the water level and corresponding flow discharges. However, the rating-curve involves many uncertainties because of the limited data especially when observed water level exceed past historical water levels. In order to compensate for insufficient data and increase the accuracy of flow discharge data, this study estimates the flow discharge in the river computed mathematically using Monte Carlo simulation based on a 1D hydrodynamic numerical model. Based on the existing rating curve, a random combination of coefficients constituting the rating-curve creates a number of virtual rating curve. From the computed results of the hydrodynamic model, it is possible to estimate flow discharge which reproduces best fit to the observed water level. Based on the statistical evaluation of these samples, a method for mathematically estimating the water level and flow discharge of all cross sections is porposed. The proposed methodology is applied to the junction of Yochoen Stream in the Seomjin River. As a result, it is confirmed that the water level reproducibility was greatly improved. Also, the water level and flow discharge can be calculated mathematically when the proposed method is applied.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.43-54
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• 2024

Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI). Materials and Methods: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC). Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10^-3 mm²/s vs. 1.23 ± 0.25 10^-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.20-31
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• 2024

Purpose: Research and development of high-strength concrete enables high-rise buildings and reduces the self-weight of the structure by reducing the cross-section, thereby reducing the thickness of beams and slabs to build more floors. A large effective space can be secured and the amount of reinforcement and concrete used to designate the base surface can be reduced. Method: In terms of field construction and quality, the effect of reducing the occurrence of drying shrinkage can be confirmed by studying the combination of low water bonding ratio and minimizing bleeding on the concrete surface. Result: The ease of site construction was confirmed due to the high self-charging property due to the increased fluidity by using high-performance water reducing agents, and the advantage of shortening the time to remove the formwork by expressing the early strength of concrete was confirmed. These experimental results show that the field application of ultra-high-strength concrete with a design standard strength of 100 MPa or higher can be expanded in high-rise buildings. Through this study, we experimented and evaluated whether ultra-high-strength concrete with a strength of 130 MPa or higher, considering the applicability of high-rise buildings with more than 120 floors in Korea, could be applied in the field. Conclusion: This study found the optimal mixing ratio studied by various methods of indoor basic experiments to confirm the applicability of ultra-high strength, produced 130MPa ultra-high strength concrete at a ready-mixed concrete factory similar to the real size, and tested the applicability of concrete to the fluidity and strength expression and hydration heat.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.99-107
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• 2024

This study analyzed how computer-based digital technology affects convergence performing arts according to the trend of the times of domestic performing arts. Based on the analyzed contents, the purpose of the study was to propose an appropriate use plan for performing arts and technology and a plan for future development of convergence performing arts. Looking at the analysis results according to the purpose of the study, as a first step, the use of video technology developed in the performing arts stage using video technology evolved into holograms, media art, and 3D techniques. In the second step, technology and art were fused using artificial intelligence and robots. Artificial intelligence composed music, choreographed dance, and wrote a play script. In addition, robots performed and played with humans on stage. Third, virtual space was also used in performing arts. It was possible to direct spaces in various places using virtual spaces rather than performance halls and stage spaces. In this way, performing arts using digital technology will become more diverse and professional, and things that are possible in imagination that cross boundaries will be developed into reality. This study proposes a convergence that appropriately utilizes various technologies of digital and computer while maintaining the area of creation that humans can do and the expressiveness and artistry they express. In preparation for these changes in the times, future convergence performing artists should be able to acquire a combination of artistry and technology of stage technology experts who can use digital technology, professional actors who can express artistry along with AI, and professionals who can create art by manipulating AI.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.1
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• pp.46-52
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• 2009

Statement of problem: Recently, titanium has become popular as superstructure material in implant dentistry because titanium superstructure can be easily milled by means of computer-aided design and manufacture (CAD/CAM) technique. But retention form such as nail head or bead cannot be cut as a result of technical limitation of CAD/CAM milling and bond strength between titanium and porcelain is not as strong as that of conventional gold or metal alloy. Purpose: The objective of this study was to evaluate the shear bond strength of three different materials: heat curing resin, composite resin, porcelain which were bonded to grade II commercially pure Titanium (CP-Ti). Material and methods: Thirty seven CP-Ti discs with 9 mm diameter, 10 mm height were divided into three groups and were bonded with heat curing resin (Lucitone 199), indirect composite resin (Sinfony), and porcelain (Triceram) which were mounted in a former with 7 mm diameter and 1 mm height. Samples were thermocycled for 1000 cycles at between $5-55^{\circ}C$. Shear bond strength (MPa) was measured with Instron Universal Testing Machine with cross head speed of 1 mm/min. The failure pattern was observed at the fractured surface and divided into adhesive, cohesive, and combination failure. The data were analyzed by one-way ANOVA and Scheffe's multiple range test (${\alpha}=0.05$). Results: Lucitone 199 ($17.82{\pm}5.13\;MPa$) showed the highest shear bond strength, followed by Triceram ($12.97{\pm}2.11\;MPa$), and Sinfony ($6.00{\pm}1.31\;MPa$). Most of the failure patterns in Lucitone 199 and Sinfony group were adhesive failure, whereas those in Triceram group were combination failure. Conclusion: Heat curing resin formed the strongest bond to titanium which is used as a CAD/CAM milling block. But the bond strength is still low compared with the bond utilizing mechanical interlocking and there are many adhesive failures which suggest that more studies to enhance bond strength are needed.