• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1141-1151
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• 2008

Data mining is the process of sorting through large amounts of data and picking out useful information. One of the well-studied problems in data mining is the exploration of association rules. An association rule technique finds the relation among each items in massive volume database. Some interestingness measures have been developed in association rule mining. Interestingness measures are useful in that it shows the causes for pruning uninteresting rules statistically or logically. This paper propose a conditional pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence and pure confidence are shown by numerical example. The results show that the conditional pure confidence is better than confidence or pure confidence.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.179-188
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• 2011

Association rule represents the relationship between items in a massive database by quantifying their relationship, and is used most frequently in data mining techniques. In general, association rule technique generates the rule, 'If A, then B.', whereas negative association rule technique generates the rule, 'If A, then not B.', or 'If not A, then B.'. We can determine whether we promote other products in addition to promote its products only if we add negative association rules to existing association rules. In this paper, we proposed the negatively pure association rules by negatively pure support, negatively pure confidence, and negatively pure lift to overcome the problems faced by negative association rule technique. In checking the usefulness of this technique through numerical examples, we could find the direction of association by the sign of the negatively pure association rule measure.

• Journal of the Korean Society of Radiology
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• v.83 no.4
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• pp.876-886
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• 2022

Purpose To evaluate the performance of MicroPure US imaging to detect and characterize microcalcifications. Materials and Methods A total of 171 lesions with suspicious microcalcifications seen on mammography and B-mode US were included and simultaneously evaluated using MicroPure US imaging. The size of microcalcifications was divided into small (punctate, amorphous, fine pleomorphic, and fine linear) and large (coarse heterogeneous), and the extent was divided into narrow (grouped) and wide (others). MicroPure US imaging visibility was divided into four types based on the number of microcalcifications on the two images: B > M (more on B-mode), B = M (similar), B < M (more on MicroPure), and negative. Triple pairwise comparison was used to evaluate the imaging features according to the MicroPure US imaging visibility. Results Among the 171 lesions examined, 157 lesions (91.8%) were detected by MicroPure US imaging. The proportion of Breast Imaging Reporting and Data System (BI-RADS) category 4A was significantly higher in the MicroPure positive group, and that of category 4B was significantly higher in the MicroPure negative group (p = 0.035). The other imaging features did not differ. Among the positive MicroPure subgroups, all features showed no significant difference. Conclusion MicroPure US imaging demonstrated 91.8% positivity in detecting microcalcifications on B-mode US. MicroPure US imaging visibility correlated with the BI-RADS category of microcalcifications.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.939-948
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• 2012

The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between items in a massive database based on the interestingness measures such as support, confidence, lift, etc. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control.In general, association rule technique generates the rule, 'If A, then B.', whereas negative association rule technique generates the rule, 'If A, then not B.', or 'If not A, then B.'. We can determine whether we promote other products in addition to promote its products only if we add negative association rules to existing association rules. In this paper, we proposed the negatively attributable and pure confidence to overcome the problems faced by negative association rule technique, and then we checked three conditions for interestingness measure. The comparative studies with negative confidence, negatively pure confidence, and negatively attributable and pure confidence are shown by numerical examples. The results show that the negatively attributable and pure confidence is better than negative confidence and negatively pure confidence.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.601-609
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• 2014

The most widely used data mining technique for big data analysis is to generate meaningful association rules. This method has been used to find the relationship between set of items based on the association criteria such as support, confidence, lift, etc. Among them, confidence is the most frequently used, but it has the drawback that we can not know the direction of association by it. The attributably pure confidence was developed to compensate for this drawback, but the value was changed by the position of two item sets. In this paper, we propose four symmetrically and attributably pure confidence measures to compensate the shortcomings of confidence and the attributably pure confidence. And then we prove three conditions of interestingness measure by Piatetsky-Shapiro, and comparative studies with confidence, attributably pure confidence, and four symmetrically and attributably pure confidence measures are shown by numerical examples. The results show that the symmetrically and attributably pure confidence measures are better than confidence and the attributably pure confidence. Also the measure NSAPis found to be the best among these four symmetrically and attributably pure confidence measures.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.11
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• pp.1521-1528
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• 2011

We studied the material properties for reliability improvement in finite element analysis results for a nitrile butadiene rubber hub-bearing seal and for a carbon-filled rubber mount used in a vehicle. It was difficult to measure the material properties of hundreds of types of rubber for the mount design. Thus, we suggested that the engineering stressstrain relations from pure shear test data could be synthesized by using simple tension data and Poisson's ratio. We defined Poisson's ratio by using a function of principal stretches to synthesize the stress-strain relations for a pure shear test. A transformation of the pure shear data was applied to the experimental values to obtain the predicted results when the strain approaches 100%. In the finite element analysis for the contact force of a hub-bearing seal, the strain results that used the transformation of the pure shear data and simple tension data almost corresponded to the experimental values. Ogden constants were used to analyze.

• Journal of Korean Biological Nursing Science
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• v.17 no.2
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• pp.132-139
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• 2015

Purpose: The purpose of this study was to verify the correlation analysis between fluid intake on skin hydration and pH of college students. Methods: The subjects were 129 female nursing students in D city. Data were collected by a self-administered questionnaire, using a skin moisture checker and skin pH meter on faces, hands, and feet. The collected data were analyzed by descriptive statistics, t-test, ANOVA, and Pearson correlation analysis, using SPSS WIN, 20. Results: The daily fluid intake was composed of 57% pure water, 21% caffeinated beverages, 22% non-caffeinated beverages. There were significant differences in average skin hydration on the three body parts according to pure water, caffeinated beverages, and non-caffeinated beverages; however, there was no significant difference measured by fluid intake. There was a significant positive correlation between fluid intake and skin hydration: between pure water and skin hydration. There was significant negative correlation between caffeinated beverages and skin hydration: between non-caffeinated beverages and skin hydration. Conclusion: The results suggest that fluid intake, pure water, caffeinated beverages, and non-caffeinated beverages have an effect on skin hydration and pH. Therefore, it is good to increase the amount of fluid intake but, it is recommended to increase the amount of intake of pure water rather than beverages to improve skin status.

• International Journal of Air-Conditioning and Refrigeration
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• v.12 no.2
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• pp.70-78
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• 2004

An experimental study on the pressure drop during flow boiling for pure refrigerants Rl34a and R123, and their mixture was carried out in a uniformly heated horizontal tube. Tests were run at a pressure of 0.6㎫ and in the ranges of heat flux 5-50㎾/$m^2$, vapor quality 0-100 percent and mass velocity of 150-600 kg/$m^2$s. Generally, the two-phase frictional multiplier is used to predict the frictional pressure drop during the two-phase flow boiling. The obtained results have been compared to the existing various correlations for the two-phase multiplier. Also, the frictional pressure drop was compared to a few available correlations; The Lockhart-Martinelli correlation considerally overpredicted the frictional pressure drop data for mixture as well as pure components in the entire mass velocity ranges employed in the present study, while the Chisholm correlation underpredicted the present data. The Friedel correlation was found to satisfactorily correlate the frictional pressure drop data except for a low quality region.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.523-532
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• 2013

Generally, data mining is the process of analyzing big data from different perspectives and summarizing it into useful information. The most widely used data mining technique is to generate association rules, and it finds the relevance between two items in a huge database. This technique has been used to find the relationship between each set of items based on the interestingness measures such as support, confidence, lift, etc. Among many interestingness measures, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The attributably pure confidence and compared confidence are able to determine the direction of the association, but their ranges are not [-1, +1]. So we can not interpret the degree of association operationally by their values. This paper propose a compared and attributably pure confidence to compensate for this drawback, and then describe some properties for a proposed measure. The comparative studies with confidence, compared confidence, attributably pure confidence, and a proposed measure are shown by numerical example. The results show that the a compared and attributably pure confidence is better than any other confidences.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.289-293
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• 2019

This paper presents a remote patient monitoring system for diagnostic pure-tone audiometry. A pure-tone audiometer was developed for basic hearing screening; its performance was evaluated according to international standards in terms of linearity, accuracy, and total harmonic distortion. Pure-tone audiometry has a maximum hearing level of 104.9 dB HL that is comparable with other commercial products. The audiometer shows satisfactory linearity with a deviation of ${\pm}0.4dB$, an accuracy of ${\pm}0.025%$, and a total harmonic distortion (THD) of 0.21%. The remote patient monitoring systems include remote control devices based on wide area network (WAN) connections and an audiometer connected in series. Through experimentation, we successfully performed real-time diagnostic communication without delay in transferring audiometric data. This system is expected to supply domestic equipment in the audiometric market and to improve the quality of life of patients in non-clinical environments.