• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.255-274
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• 2024

Purpose: Non-Fungible Token (NFT) ART, based on NFT technology, represents a novel form of art that has recently garnered significant attention in the art market. NFT technology enables the assertion of ownership over digital data, introducing the concept of ownership into the digital realm. As digital data gains ownership, NFT ART is anticipated to be positively viewed as an investment and expected to become more active due to the characteristics of this new technology. Therefore, this study aims to verify the influence of NFT ART characteristics on perceived value. This study contributes to extracting the distinctive characteristics of NFT ART compared to other forms of art and to understanding the perceived value of NFT ART among consumers with purchasing experience. Methods: This study applied structural equation modeling to explore the relationships among the variables using SPSS 26.0 and R program version 4.2.3. A total of 320 questionnaires were retrieved, all of which were adopted as valid analytical samples without missing values. Results: The findings indicate that the decentralization, transparency, and scarcity of NFT ART positively influence the perceived usefulness and enjoyment among consumers, while security does not have a significant impact. This suggests that higher levels of decentralization, transparency, and scarcity in NFT ART enhance perceived usefulness and enjoyment for consumers, significantly influencing the perceived value. Furthermore, it was confirmed that these characteristics are considered important values and perceptions from the consumer's perspective. Conclusion: The research presents positive factors for the activating of purchases among consumers considering buying NFT ART. It emphasizes the necessity of benefits for all participants to activate the art market. Additionally, the perceived value provides crucial insights for inducing active purchasing behavior in the NFT ART market and serves as a foundational study for further research.

• Journal of Animal Science and Technology
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• v.56 no.8
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• pp.28.1-28.6
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• 2014

This study was conducted to estimate the effective population size using SNPs data of 240 Jeju horses that had raced at the Jeju racing park. Of the total 61,746 genotyped autosomal SNPs, 17,320 (28.1%) SNPs (missing genotype rate of >10%, minor allele frequency of <0.05 and Hardy-Weinberg equilibrium test P-value of < $10^{-6}$) were excluded after quality control processes. SNPs on the X and Y chromosomes and genotyped individuals with missing genotype rate over 10% were also excluded, and finally, 44,426 (71.9%) SNPs were selected and used for the analysis. The measures of the LD, square of correlation coefficient ($r^2$) between SNP pairs, were calculated for each allele and the effective population size was determined based on $r^2$ measures. The polymorphism information contents (PIC) and expected heterozygosity (HE) were 0.27 and 0.34, respectively. In LD, the most rapid decline was observed over the first 1 Mb. But $r^2$ decreased more slowly with increasing distance and was constant after 2 Mb of distance and the decline was almost linear with log-transformed distance. The average $r^2$ between adjacent SNP pairs ranged from 0.20 to 0.31 in each chromosome and whole average was 0.26, while the whole average $r^2$ between all SNP pairs was 0.02. We observed an initial pattern of decreasing $N_e$ and estimated values were closer to 41 at 1 ~ 5 generations ago. The effective population size (41 heads) estimated in this study seems to be large considering Jeju horse's population size (about 2,000 heads), but it should be interpreted with caution because of the technical limitations of the methods and sample size.

• Atmosphere
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• v.24 no.3
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• pp.445-456
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• 2014

A real-time quality control system for meteorological data (air temperature, air pressure, relative humidity, wind speed, wind direction, and precipitation) measured by an integrated meteorological sensor has been developed based on comparison of quality control procedures for meteorological data that were developed by the World Meteorological Organization and the Korea Meteorological Administration (KMA), using time series and statistical analysis of a 12-year meteorological data set observed from 2000 to 2011 at the Incheon site in Korea. The quality control system includes missing value, physical limit, step, internal consistency, persistence, and climate range tests. Flags indicating good, doubtful, erroneous, not checked, or missing values were added to the raw data after the quality control procedure. The climate range test was applied to the monthly data for air temperature and pressure, and its threshold values were modified from ${\pm}2{\sigma}$ and ${\pm}3{\sigma}$ to ${\pm}3{\sigma}$ and ${\pm}6{\sigma}$, respectively, in order to consider extreme phenomena such as heat waves and typhoons. In addition, the threshold values of the step test for air temperature, air pressure, relative humidity, and wind speed were modified to $0.7^{\circ}C$, 0.4 hPa, 5.9%, and $4.6m\;s^{-1}$, respectively, through standard deviation analysis of step difference according to their averaging period. The modified quality control system was applied to the meteorological data observed by the Weather Information Service Engine in March 2014 and exhibited improved performance compared to the KMA procedures.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.59-70
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• 2010

Each pixels become got pixel value for color of only one from among colors because of bayer pattern that light receiving device of image sensor which is used in HHP and digital camera writes only one color. Information of the missing pixels could infer perfect color image from using information of neighbor pixels by using CFA(Color Filter Array). In this paper, we derive relation between the average of the data from the light receiving device of image sensor and each color channel data. And by using this relation, a new efficient edge color fuzzy method for color interpolation is proposed. Also, missing luminance signal channel interpolation was fuzzy interpolation along any edges direction for reducing color noise and interpolating efficiently it. And in this paper, the proposed method has been proved improving average 2.4dB than the conventional method by using PSNR. Also, resolution of the image of the proposed method was similar to the original image by visual images, we has been verified to be decreased a chromatic aberration than image of conventional algorithms with simulation result.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.165-173
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• 2020

ABS and PLA are applied for restoring missing part of damaged ceramics, but are not similar to the material of ceramics, so this study conducted a research on the properties and applicability of ceramic resin. This study conducted actual restoration of ABS and ceramic resin as well as cast restoration method with experiment of properties. Results show that manufacturing of restored part showed higher precision than existing materials, which enables printing of tiny shapes showing excellent surface texture and gloss than L30 and ABS resin. As a result of measuring properties, the material showed excellent durability than existing materials with no contraction and deformation and compressive strength, but value of specific gravity and hardness can lower processability after manufacturing. Long-term monitoring, evaluation of reliability of ceramic resin applied in this study, additional researches on the restorability of the original shape when printing too thin or long restored part are needed.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.909-921
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• 2010

Cross-wave regression imputation and carry-over imputation method are generally used in the analysis of panel data with missing values. Recently it is known that the BLS non-response adjust method has good statistical properties. In this paper we show that the BLS method can be considered as an imputation method with a similar formula of a ratio-estimator. In addition, we show that the carry-over imputation and BLS imputation are approximately the same under the assumption that data follow a non-stationary process with drift. Small simulation studies and real data analysis are performed. For the real data analysis, a monthly labor statistic (2007) is used.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.371-379
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• 2009

In this paper, we proposed new de-interlacing algorithm for video data using intensity gradient filter and median filter with texture detection in the image block. We first introduce the texture detection. According to texture detection, the current region is determined into smooth region or texture region. In case that the smooth region interpolated by median filter. In addition, in case of the texture region, we calculate missing pixel value using intensity gradient filter. Therefore, we analyze the local region feature using the texture detection and classify each missing pixel into two categories. And then, based on the classification result, a different de-interlacing algorithm is activated in order to obtain the best performance. Experimental results show that the proposed algorithm performs well with a variety of moving sequences compared with conventional intra-field method in the literature.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.29-37
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• 2008

To transmit video bit stream over low bandwidth such as wireless channel, high compression algorithm like H.264 codec is exploited. In transmitting high compressed video bit-stream over low bandwidth, packet loss causes severe degradation in image quality. In this paper, a new algorithm for recovery of missing or erroneous motion vector is proposed. Considering that the missing or erroneous motion vectors in blocks are closely correlated with those of neighboring blocks. Motion vector of neighboring blocks are clustered according to average linkage algorithm clustering and a representative value for each cluster is determined to obtain the candidate motion vector sets. As a result, simulation results show that the proposed method dramatically improves processing time compared to existing H.264/AVC. Also the proposed method is similar to existing H.264/AVC in terms of visual quality.

• The Journal of Advanced Prosthodontics
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• v.5 no.1
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• pp.51-57
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• 2013

PURPOSE. The purpose of this study was to to analyze the effect of Type 2 diabetes on tooth mortality, implant treatment and prosthetic status. MATERIALS AND METHODS. 275 Type 2 diabetics and 300 non-diabetics, aged 40-80 years were selected for analysis. The assessment of number of teeth, missing teeth, fixed prostheses (bridge pontics), implants using panoramic radiographs and dental records were carried out. RESULTS. Diabetes mellitus (DM) patients had a higher number of missing teeth (P<.05) and placed implants (P=.074), age (P<.05), male gender percentage (P=.042), smoker percentage (P<.05) than non-DM patients. In univariate analysis, the patients in older group showed significantly higher number of tooth loss rate at the first dental examination than the patients in younger group. Tooth loss rate of smokers did not show higher value than that of non-smokers. When multiple variables including DM, age, smoking, gender were considered together, diabetics and older group patients showed significantly higher tooth loss rate at the first dental examination than non-diabetics and younger group patients, respectively. Smokers and male group did not show a significant difference than nonsmokers and female group, respectively. CONCLUSION. Tooth mortality and implant treatment rate were significantly higher in the DM group as indicated by univariate and multivariate logistic regression analysis. Old age groups showed significantly higher odds ratios and tooth loss rate. As diabetics showed the higher tooth loss rate than non-diabetics, diabetics also had more implant restorations than non-diabetics.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.1-6
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• 2019

The proposed model is developed to minimize the loss of information in incomplete data including missing data. The first step is to transform the learning data to compensate for the loss information using the data extension technique. In this conversion process, the attribute values of the data are filled with binary or probability values in one-hot encoding. Next, this conversion data is input to the deep learning model, where the number of entries is not constant depending on the cardinality of each attribute. Then, the entry values of each attribute are assigned to the respective input nodes, and learning proceeds. This is different from existing learning models, and has an unusual structure in which arbitrary attribute values are distributedly input to multiple nodes in the input layer. In order to evaluate the learning performance of the proposed model, various experiments are performed on the missing data and it shows that it is superior in terms of performance. The proposed model will be useful as an algorithm to minimize the loss in the ubiquitous environment.