• The Journal of Korean Academy of Prosthodontics
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• v.53 no.3
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• pp.198-206
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• 2015

Purpose: We aimed to investigate the effect of combined various microgrooves and thermal oxidation on the titanium (Ti) and to evaluate various in vitro responses of human periodontal ligament cells (PLCs). Materials and methods: Grade II titanium disks were fabricated. Microgrooves were applied on titanium discs to have $0/0{{\mu}m}$, $15/3.5{{\mu}m}$, $30/10{{\mu}m}$, and $60/10{{\mu}m}$ of respective width/depth by photolithography. Thermal oxidation was performed on the microgrooves of Ti substrata for 3 h at $700^{\circ}C$ in air. The experiments were divided into 3 groups: control group (ST), thermal oxidation group (ST/TO), and combined microgrooves and thermal oxidation group (Gr15-TO, Gr30-TO, Gr60-TO). Surface characterization was performed by field-emission scanning microscopy. Cell adhesion, osteoblastic differentiation, and mineralization were analyzed using the bromodeoxyurdine (BrdU), Alkaline phosphatase (ALP) activity, and extracellular calcium deposition assays, respectively. Statistical analysis was performed using the oneway analysis of variance and Pearson's bivariate correlation analysis (SPSS Version 17.0). Results: In general, the combined microgrooves and thermal oxidation group (Gr15-TO, Gr30-TO, Gr60-TO) showed significantly higher levels compared with the control (ST) or thermal oxidation (ST-TO) groups in the BrdU expression, ALP activity, and extracellular calcium deposition. Gr60-TO group induced highest levels of cell adhesion and osteoblastic differentiation. Conclusion: Within the limitation of this study, we conclude that the Ti surface treatment using combined microgrooves and thermal oxidation is highly effective in inducing the cell adhesion andosteoblastic differentiation. The propose surface is also expected to be effective in inducing rapid and strong osseointegration of Ti oral implants.

• Korean Journal of Ecology and Environment
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• v.46 no.4
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• pp.561-569
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• 2013

This study examines locational environment factors that may affect the vegetation structure in the forests of Naejang National Park. To that end, we selected LAI (Leaf Area Index), diameter at breast height, and tree height as structural variables as well as altitude above sea level, gradient, slope direction, soil moisture, topographic location, and amount of solar radiation as locational environment factors, using the method of canonical correlation analysis in order to find out correlation between them. As to the simple correlation between the locational environment factors and structural variables, the correlation coefficient was relatively low (0.6). The values of LAI, measured along the ridge with higher altitudes, decreased as the soil moisture and solar radiation increased. However, LAI increased as the gradient increased and the slope direction faced the north (farther from the east). In respect of the diameter at breast height, the diameter decreased as the altitude and gradient increased. But the diameter increased as the moisture and solar radiation increased. The tree height decreased as the moisture increased and the site was closer to the ridge. These various correlations show a variety of locational environment factors in the national park, implying that the structural variables are affected by complex locational environment factors. This study conducted a canonical correlation analysis on locational environment factors which may affect the vegetation structure, and the result showed that LAI increased and tree height & diameter at breast height decreased as the solar radiation & moisture decreased and altitude increased. Although more factors that may affect vegetation structure (e.g. climate) should be taken into account, this study is significant in that the vegetation structure, which can adapt to more unfavorable conditions in terms of solar radiation, moisture, and higher altitudes, could be inferred in a statistical way. The results of this study, especially the locational environment factors based on DEM, can be used for assessing diversity of vegetation structure in a forest and for monitoring the structure in a national park on a regular basis so as to establish more effective maintenance plans of a park.

• Korean Journal of Social Welfare
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• v.60 no.3
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• pp.31-58
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• 2008

This study examines applying the elderly poverty labor hypothesis and skill-biased technological change hypothesis to labor force participation rate(LFPR) of older males in Korea. These hypotheses have hardly been examined on the this group. The analysis is based on the data "Summary of economically active population($1965{\sim}2007$)", "Population projection($1965{\sim}2007$)", "Report on wage structure survey($1993{\sim}2005$)" and "Korea Labor and Income Panel Study($1998{\sim}2006$)". The method employed for this study is logistic regression. The main results from this analysis are summarized in five points. Firstly, Korean older males' LFPR have been increasing since 1965 when industrialization was expanding at full steam. This trend has been different from the decreasing trend of industrialized countries. The second finding is that poor older males' LFPR is, on the average, 5.2% higher than that of non-poor older males from 1998 to 2005. The third result is that the non-elderly man has been increasingly positioned at higher grade occupations, while the elderly man has been held at lower grade occupations. The fourth is that labor demand for highly educated workers has exceeded the increased labor supply of the group, while the demand for low educated workers has decreased far beyond the declined labor supply. As a result, college premium has increased from 139% in 1993 to 157.8% in 2005. The final main implication of this study is that the industrialization theory and modernization hypothesis still holds for the LFPR of Korean older males. However, the elderly affluence hypothesis of the LFPR of older males are hardly persuasive in explaining Korean phenomenon. Especially, we find that the elderly poverty is the main mechanism in determining the Korean LFPR in old ages. This supports the elderly poverty labor hypothesis presented in this study. Skill-biased technological change hypothesis partially explains the LFPR of older man. However, we believe that other factors; human capital specially high school education rather than university education and skill required in less skill biased occupations or the poverty; also have taken effect.

• Journal of Preventive Medicine and Public Health
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• v.30 no.3 s.58
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• pp.497-507
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• 1997

This study was conducted to evaluate the association of various lifestyle with blood pressure. The data were obtained from the individuals who got routine health examination in Department of Occupational Medicine, Yeungnam University Hospital from June to September, 1996. Among these people, we selected 130 cases of hypertensives (97 males, 33 females) and 150 normotensives(70 males, 80 females) and study was conducted. The authors collected the information of the risk factors related to hypertension such as age, family history of hypertension, fasting blood sugar, serum total cholesterol, alcohol consumption(g/week), smoking history, relative amount of salt intake (low, moderate, high), the frequency' of weekly meat consumption, BMI, daily coffee consumption(cups/day) and the frequency of regular exercise(frequency/week) through questionnaire and laboratory test. By simple analysis, BMI was significantly associated with hypertension in male(p<0.05), and the frequency of weekly meat consumption was significantly associated with hypertension in female(p<0.05). Using logistic regression model, elevated odds ratio was noted for fasting blood sugar, serum total cholesterol, family history of hypertension, alcohol consumption, salt intake and BMI, and reduced odds ratio was noted for coffee consumption and exercise in male but fasting blood sugar(odds ratio=1.022, 95% CI=1.000-1.044), family history in both of parents(odds ratio=3.301, 95% CI=1.864-4.738), salt intake(odds ratio=1.690, 95% CI=1.082-2.298) and BMI(odds ratio=1.204, 95% CI=1.065-1.343) were statistically significant(p<0.05). In female, elevated odds ratio was noted in serum total choles terol, family history of hypertension, BMI and meat consumption. Of all these variables, the family history of hypertension in either of parents(odds ratio=4.981, 95% CI=3.650-6.312), family history in both of parents(odds ratio=16.864, 95% CI=14.577-19.151), BMI(odds ratio=1.167, 95% CI=1.016-1.318) and meat consumption(odds ratio=2.045, 95% CI=1.133-2.963) showed statistically significant association with hypertension in female(p<0.05).

• Clinical and Experimental Pediatrics
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• v.49 no.6
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• pp.617-622
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• 2006

Purpose : High-Sensitivity C-reactive protein(hs-CRP) has been recognized as a very useful and sensitive predictor of the future risk of myocardial infarction. But the clinical significance of hs-CRP in children remains uncertain. To confirm the existence of obesity-induced vascular inflammation and the association between metabolic syndromes and elevation of CRP in children, we investigated the relationship among CRP, obesity, blood pressure(BP), and serum lipids in schoolboys. Methods : Twenty-eight obese(BMI $29.61{\pm}3.29kg/m^2$) and 93 non-obese(BMI $18.99{\pm}2.21kg/m^2$) boys aged 14 years were examined. Serum CRP levels was measured by the high sensitive latex turbidimetric immunoassay and subjects with CRP levels below 0.3 mg/dL were adopted to avoid the influence of acute infection. Results : Obese children had significantly higher hs-CRP levels than their non-obese group($0.104{\pm}0.075$ vs. $0.054{\pm}0.005mg/dL$). In the obese group, BMI, systolic blood pressure, diastolic blood pressure, apolipoprotein B, atherogenic index, and triglyceride were significantly higher than in nonobese. The BMI, diastolic blood pressure, apolipoprotein E, atherognic index, and triglyceride showed positive correlation with log CRP by simple regression. Multiple regression analysis indicated that BMI and apolipoprotein E were strongly related to CRP. Conclusion : This study revealed that obese children tended to have higher levels of serum hsCRP, BP elevation and dyslipidemia than the control group and that BMI and apolipoprotein E were strongly related to CRP. These results indicate that obesity related metabolic syndrome can be developed in children.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.36-44
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• 2010

In order to rehabilitate forest sites damaged by wildfire via natural or artificial restoration, it is important to determine right tree species, which can acclimate to biogeoclimatic environment at the sites. The objectives of this study were to develop site index equation of different tree species for estimating forest productivity and to provide information on species selection for post-wildfire restoration. Site index equation was developed based on environmental information from wildfire damaged areas in Gangneung, Goseong, Donghae, and Samcheok, where were located in east coastal areas of South Korea. Despite the small numbers (4~5) of environmental variables used for the development of the site index equations, statistical analysis (e.g. mean difference, standard deviation of difference, and standard error of difference) showed relatively low bias and variation, suggesting that those equations can provide relatively high capability of estimation and practical applicability with high effectiveness. The small numbers of the variables enabled the model to be applied in a wide range of usages including determination of appropriate tree species for post-wildfire restoration. The estimation of forest site productivity showed the possibility of large distribution in east coastal region as the best site for Korean ash (Fraxinus rhynchophylla) and original oak (Quercus variabilis) that can be used for firebreak in the region. These results imply that damages by forest fire can be reduced significantly by replacing existing pure coniferous forests in the area with ones dominated by broad-leaved deciduous stands, which can play an important role as fire break and/or prevent a transition from surface fire to crown fire.

• Restorative Dentistry and Endodontics
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• v.32 no.3
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• pp.222-235
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• 2007

The objectives of this study was to evaluate the effect of thermocycling on the ${\mu}TBS$ (microtensile bond strength) to dentin with four different adhesive systems to examine the bonding durability. Freshly extracted $3^{rd}$ molar teeth were exposed occlusal dentin surfaces, and randomly distributed into 8 adhesive groups 3-steps total-etching (Scotchbond Multi-Purpose Plus; SM, All Bond-2; AB), 2-steps total-etching (Single Bond; SB, One Step plus; OS), 2-steps self-etching (Clearfil SE Bond; SE, AdheSE AD) and single-step self-etching systems (Promp L-Pop; PL, Xeno III; XE) Each adhesive system in 8 adhesives groups was applied on prepared dentin surface as an instruction and resin composite (Z250) was placed incrementally and light-cured. The bonded specimens were sectioned with low-speed diamond saw to obtain $1\times1mm$ sticks after 24 hours of storage at $37^{\circ}C$ distilled water and proceeded thermocycling at the pre-determined cycles of 0, 1,000 and 2,000. The ${\mu}TBS$ test was carried out with EZ-tester at 1mm/min. The results of bond strength test were statistically analyzed using one-way ANOVA/ Duncan's test at the a < 0.05 confidence level. Also, the fracture mode of debonded surface and the interface were examined under SEM. The results of this study were as follows ; 1. 3-step total etching adhesives showed stable, but bond strength of 2-step adhesives were decreased as thermocycling stress. 2. SE showed the highest bond strength, but single step adhesives (PL, XE) had the lowest value both before and after thermocycling. 3 Most of adhesives showed adhesive failure. The total-etching systems were prone to adhesive failure and the single-step systems were mixed failure after thermocycling. Within limited results of this study, the bond strength of adhesive system was material specific and the bonding durability was affected by the bonding step/ procedure of adhesive Simplified bonding procedures do not necessarily imply improved bonding performance.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.