• The Journal of Korean Academy of Prosthodontics
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• v.46 no.1
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• pp.42-52
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• 2008

Purpose: Resonance Frequency Analysis(RFA) technique can be used as an effective method in measuring the implant stability and documenting the clinical results. This technique also determines how stable the implant is before performing a prosthetic practice. Having become one the guidelines of the implant therapy whose final objective is the immediate loading, the $Osstell^{TM}$ mentor is giving a lot of information to the clinicians recently. In this communication, experiments were performed to investigate how reliable the measured ISQ values by $Osstell^{TM}$ mentor are, and to see if those are also stable even after sterilization. As five objectives: 1) How stable measured ISQ values after fixation $Smartpeg^{TM}s$ for 400 times. 2) How stable measured ISQ values after 'attach-detach'$Smartpeg^{TM}'s$ for 400 times. 3) How stable measured ISQ values after clinical sterilization methods. 4) How stable measured ISQ values after repeatedly sterilization in autoclave for 10 times. 5) What is the critical temperature which is lost the magnetism of $Smartpeg^{TM}$. Materials and Methods: Clinical sterilization methods(Autoclave sterilization, Dentistar sterilization, Ultra violet sterilization, Vacuum dry unit sterilization, Boiling water sterilization, combined $H_{2}O_{2}$ and Alcohol sterilization).$Smartpeg^{TM}s$. D3 Block bone($3{\times}9{\times}2cm$). Osstem implant(${\emptyset}4.1$-10mm).$Osstell^{TM}$ mentor. Individual experiment was used 8 number of $Smartpeg^{TM}s$ and they had measured to ISQ values of before experiment and after experiment. Results: 1. The measured ISQ values did not change after fixation $Smartpeg^{TM}s$ for 400 times. 2. There was no significant changes in the measured ISQ values of 'attach-detach $Smartpeg^{TM}s'$ for 400 times. 3. The measured ISQ values did not change after the usual clinical sterilization methods. 4. The measured ISQ values did not change after sterilization in autoclave for 10 times. 5. It was impossible to exactly measure the critical temperature which is lost the magnetism of $Smartpeg^{TM}s$. But, the results was resulted to lost its magnetism in higher temperature than $150^{\circ}C$/10 minute. Conclusion: The measured ISQ values showed insignificant differences in case of no changes in the magnetism of the $Smartpeg^{TM}s$. It seems that the $Smartpeg^{TM}s$ can be used repeatedly in every measurement if the original magnetisms of the $Smartpeg^{TM}s$ can be recognized. There seems to be no significant changes in the measured ISQ values of 'attach-detach $Smartpeg^{TM}s'$ only if the screw pitches were unimpaired. The clinical sterilization methods seems acceptable because the result was resulted to lost its magnetism in higher temperature than $150^{\circ}C$/10minute.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.2
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• pp.267-281
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• 2019

Energetic mesoscale eddies in the East Sea (ES) associated with strong mesoscale variability impacting circulation and environments were statistically characterized by analyzing satellite altimeter data collected during 1993-2017 and in-situ data obtained from four cruises conducted between 2015 and 2017. A total of 1,008 mesoscale eddies were detected, tracked, and identified and then classified into 27 groups characterized by mean lifetime (L, day), amplitude (H, m), radius (R, km), intensity per unit area (EI, $cm^2/s^2/km^2$), ellipticity (e), eddy kinetic energy (EKE, TJ), available potential energy (APE, TJ), and direction of movement. The center, boundary, and amplitude of mesoscale eddies identified from satellite altimeter data were compared to those from the in-situ observational data for the four cases, yielding uncertainties in the center position of 2-10 km, boundary position of 10-20 km, and amplitude of 0.6-5.9 cm. The mean L, H, R, EI, e, EKE, and APE of the ES mesoscale eddies during the total period are $95{\pm}104$ days, $3.5{\pm}1.5cm$, $39{\pm}6km$, $0.023{\pm}0.017cm^2/s^2/km^2$, $0.72{\pm}0.07$, $23{\pm}21TJ$, and $588{\pm}250TJ$, respectively. The ES mesoscale eddies tend to move following the mean surface current rather than propagating westward. The southern groups (south of the subpolar front) have a longer L, larger H, R, and higher EKE, APE; and stronger EI than those of the northern groups and tend to move a longer distance following surface currents. There are exceptions to the average characteristics, such as the quasi-stationary groups (the Wonsan Warm, Wonsan Cold, Western Japan Basin Warm, and Northern Subpolar Frontal Cold Eddy groups) and short-lived groups with a relatively larger H, higher EKE, and APE and stronger EI (the Yamato Coastal Warm, Central Yamato Warm, and Eastern Japan Basin Coastal Warm eddy groups). Small eddies in the northern ES hardly resolved using the satellite altimetry data only, were not identified here and discussed with potential over-estimations of the mean L, H, R, EI, EKE, and APE. This study suggests that the ES mesoscale eddies 1) include newly identified groups such as the Hokkaido and the Yamato Rise Warm Eddies in addition to relatively well-known groups (e.g., the Ulleung Warm and the Dok Cold Eddies); 2) have a shorter L; smaller H, R, and lower EKE; and stronger EI and higher APE than those of the global ocean, and move following surface currents rather than propagating westward; and 3) show large spatial inhomogeneity among groups.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.