• Journal of dental hygiene science
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• v.13 no.2
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• pp.135-141
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• 2013

One factor for successful prognosis of finished dental prosthesis is good marginal fit. The purpose of this study in vitro investigation was to compare the marginal fit of all-ceramic crown before and after porcelain veneering, to evaluate the influence of high temperature of the porcelain firing on the fit. For this experiment, model of abutment tooth of maxillary right central incisor was prepared. Ten working models were produced. Ten zirconia cores were made by dental computer aided design/computer aided manufacturing system. The marginal fit of specimens were examined using silicone replica technique. Silicone replicas were sectioned four times and were measured through a digital microscope (${\times}160$). Marginal fit is a distance connected between edge end part of specimen and abutment margin. Each specimens was measured twice, the first measurement was done prior to veneering porcelain firing, while the second measurement was done after the porcelain firing to evaluate this process. Statistical analyses were performed with paired t-test. $Mean{\pm}SD$ marginal fit was $60.8{\pm}14.2{\mu}m$ for zirconia core and $86.1{\pm}13.3{\mu}m$ for all-ceramic crown. They were statistically significant differences (p<0.001). But all specimens showed a marginal fit where the gap widths ranged within the clinical recommendation ($120{\mu}m$), all-ceramic crown production using the zirconia core was adequate.

• Journal of Distribution Science
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• v.13 no.11
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• pp.101-109
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• 2015

Purpose - A traditional retail market is a place that offers economic opportunity to employees and employers alike it also is a place where the community can meet. The Korean government has invested three trillion won to improve physical and non-physical aspects in traditional retail markets since 2004. However, little research on this has been conducted. We explore this research gap that could lead to theory extension. We analyze consumption behavior with respect to traditional retail markets through an empirical analysis, thus overcoming limits in previous research. We empirically analyze policy effects of traditional retail market projects supported by the Korean government. Research design, data, and methodology - We propose a traditional retail market improvement plan via the relation between cause and effect resulting from the analysis. More specifically, logit analysis was carried out with 1,754 consumers in 16 cities nationwide. In order to analyze consumer consumption behaviors nationwide, the probability was analyzed using a logit model. This research analyzes the link between support and non-support by the Korean government using binary values. The dependent variable is whether Korean government support is implemented; the binomial logistic regression is used as the statistical estimation technique. The object variables are:1 (support) or 0 (nonsupport), and the prediction value is between 1 and 0. As a result of the factor analysis of questions related to attributes of service quality, four factors were extracted: convenience, product, facilities, and service. Results - The results indicate that convenience, product, and facilities have a significant influence on consumer satisfaction in accordance with the government's traditional retail market support. Additionally, the results reveal that convenience, product, facilities, and service all have a significant influence on consumer satisfaction in a traditional retail market's service quality and consumer satisfaction. Finally, the analysis indicates that the highly satisfied traditional retail market customer has a significant influence on revisit intention. Moreover, the results reveal that the highly satisfied traditional retail market customer has a significant influence on recommendation intention. Conclusions - This research focused on consumers nationwide to measure policy effects of traditional retail markets compared to previous research that focused on one traditional retail market or a specific area. We verified the relationship of service quality and customer satisfaction and consumer behavior based on service quality theory. The results indicate that consumer satisfaction of traditional retail markets supported by service quality factors has a significant impact. In a concrete form, the results indicate that these effects are from facility modernization projects and marketing support projects of the Korean government. The results also imply that these facility and management support effects from the Korean government have been consistent. We realize that the Korean government has to selectively support traditional retail markets in major cities and small and medium-sized cities. To that end, the Korean government needs to select a concentration strategy for the revitalization of traditional retail markets.

• Journal of agricultural medicine and community health
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• v.10 no.1
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• pp.26-35
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• 1985

The present study was undertaken to evaluate the prevalence rate of Taenia species infection and distribution of taeniasis caused by Taenia solium among Koreans in Seoul and Cheju Do In Korea during the period from August to December 1984. A total of 4,256 stool specimens from the students of Mapo Ku and 360 stool specimens from the inhabitants of Guro-Ku in Seoul was collected and examined by cellophane thick smear technique. On the other hand 1,015 stool specimens from the students of Jocheon Myun and 265 specimens from the students of Jocheon Myun and 265 specimens from the inhabitants of Aeweol Myun and Gujwa Myun in Cheju Do were also examined. The results were summarized as follows ; Four (0.1%) out of 4.256 students were positive and 2 (0.6%) out of 360 inhabitants in Seoul were positive (Table 1, 2). Positive rates of taeniasis in Cheju Do were 1.7% (17) out of 1,015 students and 12.5% (33) out of 5 villagers (Table 3, 4). In order to observe the distribution of Taenia solium infection, the scolex or a part of Taenia spp. were collected from the stool of positive cases by anthelmintic treatment. For the species identification, expelled proglottides were examined microscopically by the number of branches of the uterus, presence of vaginal sphincter or the accessory ovarian lobe etc. Three cases were infected with Taenia solium among 6 egg positive cases in Seoul. But only 1 case was infected with Taenia solium out of 7 students taking anthelmintics voluntarily by recommendation of Korean Association for Parasite Eradication (KAFPE). On the other hand, among 32 cases of egg positive cases of this study and 26 cases of KAFPE in Cheju Do, 13 cases (22.4%) were infected with Taenia solium. But 13 cases who were not examined and complained expulsion of proglottides in their stool were infected with Taenia saginata (Table 5). Among 62 persons infected with Taenia saginata, only 4 cases did not know their infection of this worm. Rut in 17 cases with Taenia solium, 7 persons did not know their infection until stool examinations were performed (Table 6).

• Knowledge Management Research
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• v.21 no.3
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• pp.177-195
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• 2020

The social consciousness on fake reviews has triggered researchers to suggest ways to cope with them by analyzing contents of fake reviews or finding ways to discover them by means of structural characteristics of them. This research tried to collect data from blog posts in Naver and detect habitual patterns users use unconsciously by variables extracted from blogs and blog posts by a machine learning model and wanted to use the technique in predicting fake reviews. Data analysis showed that there was a very high relationship between the number of all the posts registered in the blog of the writer of the related writing and the date when it was registered. And, it was found that, as model to detect advertising reviews, Random Forest is the most suitable. If a review is predicted to be an advertising one by the model suggested in this research, it is very likely that it is fake review, and that it violates the guidelines on investigation into markings and advertising regarding recommendation and guarantee in the Law of Marking and Advertising. The fact that, instead of using analysis of morphemes in contents of writings, this research adopts behavior analysis of the writer, and, based on such an approach, collects characteristic data of blogs and blog posts not by manual works, but by automated system, and discerns whether a certain writing is advertising or not is expected to have positive effects on improving efficiency and effectiveness in detecting fake reviews.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.1-14
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• 2007

Character images for the background screen of cell phones are one of the fast growing sectors of the mobile content market. However, character image buyers currently experience tremendous difficulties in searching for desired images due to the awkward image search process. Content-based image retrieval (CBIR) widely used for image retrieval could be a good candidate as a solution to this problem, but it needs to overcome the limitation of the mobile Internet environment where an initial query set (IQS) cannot be easily provided as in the PC-based environment. We propose a new approach, IQS-AutoGen, which automatically generates an initial query set for CBIR on the mobile Internet. The approach applies the collaborative filtering (CF), a well-known recommendation technique, to the CBIR process by using users' preference information collected during the relevance feedback process of CBIR. The results of the experiment using a PC-based prototype system show that the proposed approach successfully satisfies the initial query requirement of CBIR in the mobile Internet environment, thereby outperforming the current image search process on the mobile Internet.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.118-129
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• 2022

The purpose of this report is to study a strategic model of promotion activities through various analysis and sales forecasting by selecting wearable products for domestic online companies and collecting sales data. For data analysis, various algorithms are used for analysis and the results are selected as the optimal model. The gradation boosting model, which is selected as the best result, will allow nine independent variables to be entered, including promotion type, price, amount, gender, model, company, grade, sales date, and region, when predicting dependent variables through supervised learning. In this study, the review values set as dependent variables for each type of sales promotion were studied in more detail through the ensemble analysis technique, and the main purpose is to analyze and predict them. The purpose of this study is to study the grades. As a result of the analysis, the evaluation result is 95% of AUC, and F1 is about 93%. In the end, it was confirmed that among the types of sales promotion activities, value-added benefits affected the number of reviews and review grades, and that major variables affected the review and review grades.

• Journal of the Korean Geotechnical Society
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• v.23 no.2
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• pp.47-60
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• 2007

Conventional active surface wave measurements performed using a transient or continuous source are often limited in the maximum depth of penetration due to the difficulty of generating low-frequency energy with reasonably portable sources. This limitation may inhibit accurate seismic site response calculations because of the inability to define deeper subsurface structure. By measuring surface wave generated by passive sources including microtremors and cultural noise, it is possible to overcome this problem and develop soil stiffness profiles to much larger depth. Reliability of dispersion estimates from the passive surface wave measurements is critical to present reliable shear wave velocity profiles and can be improved by the measurements and analyses of passive surface waves based on correct understanding of systematic errors included in passive dispersion data. In this study, the systematic errors caused by poor wavenumber resolution and energy leakage into sidelobes in passive tests are mainly explored. Recommendations for reliable passive surface wave measurements and dispersion estimates are presented and illustrated at a site in San Jose, California, U.S.

• Clinical Endoscopy
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• v.55 no.6
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• pp.801-809
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• 2022

Background/Aims: Current society guidelines recommend antibiotic prophylaxis for 3 to 5 days after endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) of pancreatic cystic lesions (PCLs). The overall quality of the evidence supporting this recommendation is low. In this study, we aimed to assess cyst infection and adverse event rates after EUS-FNA of PCLs among patients treated with or without postprocedural prophylactic antibiotics. Methods: We retrospectively reviewed all patients who underwent EUS-FNA of PCLs between 2015 and 2019 at two large-volume academic medical centers with different practice patterns of postprocedural antibiotic prophylaxis. Data on patient demographics, cyst characteristics, fine-needle aspiration technique, periprocedural and postprocedural antibiotic prophylaxis, and adverse events were retrospectively extracted. Results: A total of 470 EUS-FNA procedures were performed by experienced endosonographers for the evaluation of PCLs in 448 patients, 58.7% of whom were women. The mean age was 66.3±12.8 years. The mean cyst size was 25.7±16.9 mm. Postprocedural antibiotics were administered in 274 cases (POSTAB+ group, 58.3%) but not in 196 cases (POSTAB- group, 41.7%). None of the patients in either group developed systemic or localized infection within the 30-day follow-up period. Procedure-related adverse events included mild abdominal pain (8 patients), intra-abdominal hematoma (1 patient), mild pancreatitis (1 patient), and perforation (1 patient). One additional case of pancreatitis was recorded; however, the patient also underwent endoscopic retrograde cholangiopancreatography. Conclusions: The incidence of infection after EUS-FNA of PCLs is negligible. Routine use of postprocedural antibiotics does not add a significant benefit.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.4
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• pp.153-178
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• 2018

Most operational uses of wind speed data require measurements at, or estimates generated for, the reference height of 10 m above mean sea level (AMSL). On the Ieodo Ocean Research Station (IORS), wind speed is measured by instruments installed on the lighthouse tower of the roof deck at 42.3 m AMSL. This preliminary study indicates how these data can best be converted into synthetic 10 m wind speed data for operational uses via the Korea Hydrographic and Oceanographic Agency (KHOA) website. We tested three well-known conventional empirical neutral wind profile formulas (a power law (PL); a drag coefficient based logarithmic law (DCLL); and a roughness height based logarithmic law (RHLL)), and compared their results to those generated using a well-known, highly tested and validated logarithmic model (LMS) with a stability function (${\psi}_{\nu}$), to assess the potential use of each method for accurately synthesizing reference level wind speeds. From these experiments, we conclude that the reliable LMS technique and the RHLL technique are both useful for generating reference wind speed data from IORS observations, since these methods produced very similar results: comparisons between the RHLL and the LMS results showed relatively small bias values ($-0.001m\;s^{-1}$) and Root Mean Square Deviations (RMSD, $0.122m\;s^{-1}$). We also compared the synthetic wind speed data generated using each of the four neutral wind profile formulas under examination with Advanced SCATterometer (ASCAT) data. Comparisons revealed that the 'LMS without ${\psi}_{\nu}^{\prime}$ produced the best results, with only $0.191m\;s^{-1}$ of bias and $1.111m\;s^{-1}$ of RMSD. As well as comparing these four different approaches, we also explored potential refinements that could be applied within or through each approach. Firstly, we tested the effect of tidal variations in sea level height on wind speed calculations, through comparison of results generated with and without the adjustment of sea level heights for tidal effects. Tidal adjustment of the sea levels used in reference wind speed calculations resulted in remarkably small bias (<$0.0001m\;s^{-1}$) and RMSD (<$0.012m\;s^{-1}$) values when compared to calculations performed without adjustment, indicating that this tidal effect can be ignored for the purposes of IORS reference wind speed estimates. We also estimated surface roughness heights ($z_0$) based on RHLL and LMS calculations in order to explore the best parameterization of this factor, with results leading to our recommendation of a new $z_0$ parameterization derived from observed wind speed data. Lastly, we suggest the necessity of including a suitable, experimentally derived, surface drag coefficient and $z_0$ formulas within conventional wind profile formulas for situations characterized by strong wind (${\geq}33m\;s^{-1}$) conditions, since without this inclusion the wind adjustment approaches used in this study are only optimal for wind speeds ${\leq}25m\;s^{-1}$.