• Journal of Technologic Dentistry
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• v.37 no.1
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• pp.9-15
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• 2015

Purpose: The purpose of this study was to compare the accuracy of the maxillary three-unit fixed dental prosthesis (FDPs) made using two CAD/CAM milling machines : DCM Group(Dentaim CAD/CAM milling machine), WCM Group(Wieland CAD/CAM milling machine). Methods: Each of 10 duplicate models was scanned by blue light scanner(Identica, Medit, Korea), and the three-unit FDPs (STL file) was designed using DelcamCAD. A total of 20 three-unit FDPs was fabricated, comprising 2 groups of 10 specimens each (shrinkage ratio is 1:1). The first three-unit FDPs STL file was used as a CAD reference model (CRM). Obtained STL files by scanning the inner surface of three-unit FDPs were convened into the point clouds-ASC II files. Discrepancies between the point clouds and CRM were measured by superimposition software. Statistical methods to analyze the data were used non-parametric method. The mean (SD) values were compared by a Mann-Whitney U-test. Type one error rate was set at 0.05. Results: WCM group had small discrepancies with $2.17{\mu}m$ of mean value compared to $4.44{\mu}m$ in DCM group. The accuracy values between the two groups showed a sratistically significant difference (Table 2, p<.05). Conclusion: The accuracy of the three-unit fixed dental prosthesis(FDPs) made of two CAD/CAM milling machines were statistically different. Accuracy with which the prosthesis made of WCM group was superior.

• Korean Journal of Applied Biomechanics
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• v.19 no.4
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• pp.729-738
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• 2009

The purpose of this study was to analyze the impact accuracy and kinematic parameters of skill level and eye-tracking type during putting strokes. For comparison, five elite golfers and five novice golfers participated in this study. Three-dimensional kinematic data were collected for each subject while 10 putting trials were performed for each skill level and eye-tracking type. The APAS system was used to compute the impact accuracy and kinematic parameters of putter heads. The putting stroke was divided into three phases: back swing, downswing, and follow-through. The findings indicated that significant differences were found in skill level as it affected the rate of success. For impact accuracy and the displacement of putter heads, a significant difference was found for the skill level, particularly in backs-wing and follow-through. In addition, the displacement of the putter head had a greater influence on stroke accuracy than on velocity.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.249-256
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• 2018

This study aims to explore efficient foreign language learning paradigm with a focus on reading fluency and accuracy. From a perspective of language acquisition in the foreign language context, the priority in the L2 learning between accuracy and fluency has been a very important issue. Fluency becomes an important issue due to many researchers' interests in the L1 and L2 classroom. Although both accuracy and fluency are crucial, the paradigm shift from fluency to accuracy is necessary in the foreign language teaching. In this context, as an alternative methodology for L2 learners' fluency, the extensive reading approach is provided. A number of studies have suggested that extensive reading program could lead to improvement of L2 learners' reading rate and is an effective approach to improving general language proficiency.

• Journal of Preventive Medicine and Public Health
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• v.31 no.3 s.62
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• pp.471-480
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• 1998

This study was undertaken in order to estimate the accuracy of disease code of the Korean National Medical Insurance Data and disease the characteristics related to the accuracy. To accomplish these objectives, 2,431 cases coded as notifiable acute communicable diseases (NACD) were randomly selected from 1994 National Medical Insurance data file and family medicine specialists reviewed the medical records to confirm the diagnostic accuracy and investigate the related factors. Major findings obtained from this study are as follows : 1. The accuracy rate of disease code of NACD in National Medical Insurance data was very low, 10.1% (95% C.I. : 8.8-11.4). 2. The reasons of inaccuracy in disease code were 1) claiming process related administrative error by physician and non-physician personnel in medical institutions (41.0%), 2) input error of claims data by key punchers of National Medical Insurer (31.3%) and 3) diagnostic error by physicians (21.7%). 3. Characteristics significantly related with lowering the accuracy of disease code were location and level of the medical institutions in multiple logistic regression analysis. Medical institutions in Seoul showed lower accuracy than those in Kyonngi, and so did general hospitals, hospitals and clinics than tertiary hospitals. Physician related characteristics significantly lowering disease code accuracy of insurance data were sex, age group and specialty. Male physicians showed significantly lower accuracy than female physicians; thirties and fortieg age group also showed significantly lower accuracy than twenties, and so did general physicians and other specialists than internal medicine/pediatric specialists. This study strongly suggests that a series of policies like 1) establishment of peer review organization of National Medical Insurance data, 2) prompt nation-wide expansion of computerized claiming network of National Medical Insurance and 3) establishment and distribution of objective diagnostic criteria to physicians are necessary to set up a national disease surveillance system utilizing National Medical Insurance claims data.

• Journal of Applied Reliability
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• v.15 no.2
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• pp.90-100
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• 2015

Reliability growth rate (or reliability growth curve slope) have the two cases of trend as a constant or changing one during the reliability growth testing. The changing case is very common situation. The reasons of reliability growth rate changing are that the failures to follow the NHPP (None-Homogeneous Poisson Process), and the solutions implemented during test to break out other problems or not to take out all of the root cause permanently. If the changing were big, the "Goodness of Fit (GOF)" of reliability growth curve to test data would be very low and then reduce the accuracy of assessing result with test data. In this research, we are using Duane model and AMSAA model for assessing test data and projecting the reliability level of complex and repairable system as like construction equipment and vehicle. In case of no changing in reliability growth rate, it is reasonable for reliability engineer to implement the original Duane model (1964) and Crow-AMSAA model (1975) for the assessment and projection activity. However, in case of reliability growth rate changing, it is necessary to find the method to increase the "GOF" of reliability growth curves to test data. To increase GOF of reliability growth curves, it is necessary to find the proper parameter calculation method of interesting reliability growth models that are applicable to the situation of reliability growth rate changing. Since the Duane and AMSAA models have a characteristic to get more strong influence from the initial test (or failure) data than the latest one, the both models have a limitation to contain the latest test data information that is more important and better to assess test data in view of accuracy, especially when the reliability growth rate changing. The main objective of this research is to find the parameter calculation method to reflect the latest test data in the case of reliability growth rate changing. According to my experience in vehicle and construction equipment developments over 18 years, over the 90% in the total development cases are with such changing during the developing test. The objective of this research was to develop the newly assessing method and the process for GOF level increasing in case of reliability growth rate changing that would contribute to achieve more accurate assessing and projecting result. We also developed the new evaluation method for GOF that are applicable to the both models as Duane and AMSAA, so it is possible to compare it between models and check the effectiveness of new parameter calculation methods in any interesting situation. These research results can reduce the decision error for development process and business control with the accurately assessing and projecting result.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.6
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• pp.38-44
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• 2019

A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3×3 convolution filter and 2×2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.

• The Journal of Information Systems
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• v.28 no.4
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• pp.333-358
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• 2019

Purpose The study aims to develop a data-based decision model for private bankers when recommending hedge funds to their customers in financial institutions. Design/methodology/approach The independent variables are set in two groups. The independent variables of the first group are aggressive investors, active investors, and risk-neutral type investors. In the second group, variables considered by private bankers include customer propensity to invest, reliability, product subscription experience, professionalism, intimacy, and product understanding. A decision-making variable for a private banker is in recommending a first-rate general private fund composed of foreign and domestic FinTech products. These contain dependent variables that include target return rate(%), fund period (months), safeguard existence, underlying asset, and hedge fund name. Findings Based on the research results, there is a 94.4% accuracy in decision-making when the independent variables (customer rating, reliability, intimacy, product subscription experience, professionalism and product understanding) are used according to the following order of relevant dependent variables: step 1 on safeguard existence, step 2 on target return rate, step 3 on fund period, and step 4 on hedge fund name. Next, a 93.7% accuracy is expected when decision-making uses the following order of dependent variables: step 1 on safeguard existence, step 2 on target return rate, step 3 on underlying asset, and step 4 on fund period. In conclusion, a private banker conducts a decision making stage when recommending hedge funds to their customers. When examining a private banker's recommendations of hedge funds to a customer, independent variables influencing dependent variables are intimacy, product comprehension, and product subscription experience according to a categorical regression model and artificial neural network analysis model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.391-399
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• 2017

This paper presents the effects of the cutting speed and feed rate on the axial shape of flat end-milled down cut side walls. Experiments were performed using the cutting speed, tool diameter, and feed per tooth as variables, and the thrust force and axial shape were measured as the experimental results. The results of this study confirmed that a smaller feed per tooth, which is proportional to the value obtained by dividing the feed rate by the cutting speed, results in a higher axial shape accuracy. In addition, the axial shape can be simplified to a form in which two straight lines having different slopes meet at a singular point. Therefore, it was concluded that the shape accuracy could easily be estimated during the operation and improved by adjusting the feed per tooth.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.12
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• pp.1391-1400
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• 1992

In this paper, we suggested a method for evaluating the type-I/II error which is proposed by the CCITT as a criterion for the accuracy of policing algorithms in ATM networks, By the analysis of the type-I/II error of the Leaky Bucket(LB) algorithm, we investigated the relationships between the traffic parameters and the LB parameters to police the mean and peak cell rate effectively in the ON/OFF traffic. We showed that the LB parameters, the leaky rate a and the threshold M of the LB counter, could be determined as a pair of (a, M) satisfying the type-I/II error and minimizing the response time. In the ON/OFF traffic, it has been observed that the a-M characteristic curve of the LB policing algorithm only depends on the burstiness. As the results of the performance analysis, we found that the LB algorithm exhibits a good performance in the peak rate policing, but has some problems in the mean rate policing due to the trade-off between the accuracy and the response time.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.