• Journal of Technologic Dentistry
• /
• v.35 no.1
• /
• pp.37-42
• /
• 2013

Purpose: The purpose of this study was to evaluate the repeatability of dental white light scanner. Methods: The impression(Zerosil, Dreve, Germany) were digitized in white light scanner(Identica, Medit, Korea) to create 3-dimensional surface-models. The distribution of the discrepancies between the number of points in the corresponding CRM models and the point clouds in the others were measured by a matching-software(PowerInspect 2012, Delcam Plc, UK). The discriptive statistics were used for statistical analysis(SPSS 20.0). Results: The measurement of repeatablity showed very good reliability. The mean(SD) discrepancy value on the white light scanner digital models was 8.7(0.67) ${\mu}m$, based on SD and absolute mean values. Conclusion: These in vitro studies showed that repeatability of dental white light scanner is high reliability. These results can be confirmed in further clinical studies.

• Journal of Computing Science and Engineering
• /
• v.7 no.2
• /
• pp.99-111
• /
• 2013

Approximating a divergence between two probability distributions from their samples is a fundamental challenge in statistics, information theory, and machine learning. A divergence approximator can be used for various purposes, such as two-sample homogeneity testing, change-point detection, and class-balance estimation. Furthermore, an approximator of a divergence between the joint distribution and the product of marginals can be used for independence testing, which has a wide range of applications, including feature selection and extraction, clustering, object matching, independent component analysis, and causal direction estimation. In this paper, we review recent advances in divergence approximation. Our emphasis is that directly approximating the divergence without estimating probability distributions is more sensible than a naive two-step approach of first estimating probability distributions and then approximating the divergence. Furthermore, despite the overwhelming popularity of the Kullback-Leibler divergence as a divergence measure, we argue that alternatives such as the Pearson divergence, the relative Pearson divergence, and the $L^2$-distance are more useful in practice because of their computationally efficient approximability, high numerical stability, and superior robustness against outliers.

• The Journal of Korean Medicine
• /
• v.41 no.3
• /
• pp.125-137
• /
• 2020

Objectives: This study was aimed to present the experts' opinions for the successful application of Korean traditional medical practices (KTMPs) to the International Classification of Health Intervention (ICHI). Methods: Two doctors of Korean Medicine and two health information managers who had tried coding 131 KTMPs using ICHI participated in the focus group discussion. The remarks from the discussion were summarized according to the thematic analysis method. Results: The participants expected ICHI to be mainly used for statistics when applied to TKMPs. It can be used for payment systems as well, but it was expected that additional work would be required. They thought the current version of the ICHI did not sufficiently reflect the KMTPs of the real world, and even the interventions already included in the ICHI were not explained enough in the system. They thought it would not be easy to explain more KTMPs within the current structure of the ICHI, but they also said it seemed possible. In the process, rather than adding entirely new stem codes, it would be better to generate new combinations of the existing codes, to suppose subdivided codes, and to utilize the include terms or extension codes. Conclusions: For the successful introduction of ICHI, clarifying the definition of each intervention of KTMPs is a top priority. In addition, it is necessary to continue the matching work of ICHI - KMPTs and also required to make this effort together with the field of traditional medicine and complementary medicine worldwide.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.1
• /
• pp.89-99
• /
• 2015

The World Economic Forum and the Korean Ministry of Knowledge Economy have selected big data as one of the top 10 in core information technology. The key of big data is to analyze effectively the properties that do have data. Clustering analysis method of big data techniques is a method of assigning a set of objects into the clusters so that the objects in the same cluster are more similar to each other clusters. Similarity measures being used in the cluster analysis may be classified into various types depending on the nature of the data. In this paper, we studied upper and lower bounds for binary similarity measures with negative matches such as Russel and Rao measure, simple matching measure by Sokal and Michener, Rogers and Tanimoto measure, Sokal and Sneath measure, Hamann measure, and Baroni-Urbani and Buser mesures I, II. And the comparative studies with these measures were shown by real data and simulated experiment.

• The Journal of the Acoustical Society of Korea
• /
• v.18 no.8
• /
• pp.32-41
• /
• 1999

In this paper, an inversion method using chirp signals and two near field receivers is proposed. Inversion problems can be formulated into the probabilistic models composed of signals, a forward model and noise. Forward model to simulate chirp signals is chosen to be the source-wavelet-convolution planewave modeling method. The solution of the inversion problem is defined by a posteriori pdf. The wavelet matching technique, using weighted least-squares fitting, estimates the sediment sound-speed and thickness on which determination of the ranges for a priori uniform distribution is based. The genetic algorithm can be applied to a global optimization problem to find a maximum a posteriori solution for determined a priori search space. Here the object function is defined by an L₂norm of the difference between measured and modeled signals. The observed signals can be separated into a set of two signals reflected from the upper and lower boundaries of a sediment. The separation of signals and successive applications of the genetic algorithm optimization process reduce the search space, therefore improving the inversion results. Not only the marginal pdf but also the statistics are calculated by numerical evaluation of integrals using the samples selected during importance sampling process of the genetic algorithm. The examples applied here show that, for synthetic data with noise, it is possible to carry out an inversion for sedimentary layers using the proposed inversion method.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.2
• /
• pp.125-133
• /
• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So intuitive fuzzy c-means(IFCM) clustering algorithm is applied to improve KNN, which is the KNN/IFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of IFCM based on signal to noise ratio(SNR). Then, the k RPs are classified into different clusters through IFCM based on SNR. Experimental results indicate that the proposed KNN/IFCM hybrid algorithm generally outperforms KNN, KNN/FCM, KNN/PFCM algorithm when the locations error is less than 2m.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.2
• /
• pp.109-115
• /
• 2011

As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So artificial neural network(ANN) clustering algorithm is applied to improve KNN, which is the KNN/ANN hybrid algorithm presented in this paper. For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of ANN based on SNR. Then, the k RPs are classified into different clusters through ANN based on SNR. Experimental results indicate that the proposed KNN/ANN hybrid algorithm generally outperforms KNN algorithm when the locations error is less than 2m.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.20
• /
• pp.8667-8671
• /
• 2014

Background: Survival rates after resection of advanced gastric cancer are extremely poor. An increasing number of patients with gastric carcinomas (GC) are therefore being treated with preoperative chemotherapy. We evaluated 36 month survival rate of GC patients that were treated by adding a neoadjuvant chemoradiotherapy before gastrostomy.Materials and Methods: Patients with stage II or III gastric adenocarcinomas were enrolled. The patients divided into two groups: (A) Neoadjuvant group that received concurrent chemoradiation before surgery (4500cGy of radiation at 180cGy per day plus chemotherapy with cisplatin and 5-fluorouracil, in the first and the end four days of radiotherapy). Resection was attempted 5 to 6 weeks after end of chemoradiotherapy. (B) Adjuvant group that received concurrent chemo-radiation after surgical resection. Results: Two (16.7%) patients out of 12 patients treated with neoadjuvant chemo-radiotherapy and 5 (38.5%) out of 13 in the surgery group survived after 36 months. These rates were not significantly different with per protocol and intention-to-treat analysis. The median survival time of patients in group A and B were 13.4 and 21.6 months, respectively, again not significantly different. Survival was significantly greater in patients with well differentiated adenocarcinoma in group B than in group A (p<0.004). Conclusions: According to this study we suggest surgery then chemoradiotherapy for patients with well differentiated gastric adenocarcinoma rather than other approaches. Additional studies with greater sample size and accurate matching relying on cancer molecular behavior are recommended.

• Journal of the Korea Computer Industry Society
• /
• v.2 no.9
• /
• pp.1175-1182
• /
• 2001

One to one Marketing (a.k.a. database marketing or relationship marketing) is one of the many fields that will benefit from the electronic revolution and shifts in consumer sales and advertising. As a component of intelligent customer services on Internet storefront, this paper describes technology of providing personalized advertisement using the market basket analysis, a well-Known data mining technique. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customer's data. However, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed Knowledge base. In this paper, using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.21 no.4
• /
• pp.139-152
• /
• 2019

This study aims to find out the influence of brand awareness and preference of collaboration products created by SPA and luxury brands using specific examples of collaborations, which are now becoming prevelent in the fashion industry. For this study, data collection was carried out through a distribution of 350 copies of the questionnaire, 333 responses were used for data analysis. Using a statistical package program with SPSS, a frequency analysis, a factor analysis, and a multiple regression analysis were conducted. The results of this study are summarized as follows. Awareness and preferences of the SPA and luxury brands lead collaboration products to be preferred. In addition, consumers happen to have more favorable attitudes regarding the purchase of collaboration products. This means that matching brands with high preference is a very important factor to create profits from the collaboration products as awareness and the preference are important factors for the success of projects. In particular, the recognition and preference of luxury brands was found to have greater impact on the preference and recognition of collaboration the SPA brands. Accordingly, brands should expand and actively collaborate through a variety of methods and support proper collaborations that fit their image.