• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.1109-1114
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• 2009

The order to see how the deep cleansing change sebum and moisture condition by skin type, 20 persons with dry skin and oily skin were divided into two groups of scrub group and enzyme group and given the deep cleansing care, totaling 8 time. After 8 times of the deep cleansing care, the scrub group showed a valid change with oily skin while the enzyme group showed a valid change with dry skin. However, both groups did not have change a moisture with any skin types. The outcome from the questionnaire that had been done to find subjective feeling indicated that oil change resulted in a valid feeling whereas change of moisture and sensitivity were barely felt.

• Journal of Periodontal and Implant Science
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• v.51 no.4
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• pp.276-284
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• 2021

Purpose: The aim of this randomized clinical trial was to assess whether chemical cleansing using a sulfonic/sulfuric acid gel solution (HBX) as an adjunct to scaling and root planing (SRP) resulted in a decrease in residual plaque and calculus in deep periodontal pockets compared to SRP alone. Methods: Fifty-six patients with 56 hopeless posterior teeth, scheduled for extraction due to severe periodontitis, were enrolled in this study. Each tooth was randomly assigned to 1 of the 2 experimental procedures. The test teeth were subjected to the irrigation of the subgingival area with HBX for 2 minutes, followed by SRP with hand and ultrasonic instruments for 14 minutes, and then extracted. The control teeth received only mechanical instrumentation before extraction. Residual biofilm was evaluated on photographs and measured as total area and percentage of root surface covered by remaining plaque (RP) or calculus (RC) after treatment. Results: The initial pocket depth (PD) and total subgingival root surface area were similar between the 2 treatment groups. After treatment, the total subgingival root area covered by RP and RC was statistically significantly larger (P<0.001) in the control group than in the test group. The test teeth showed a lower percentage of RP, but a higher percentage of RC than the control teeth (both P<0.001). Complete calculus removal was achieved in 42% of the control teeth surfaces and in 25% of the test teeth surfaces for a PD of 4 mm. Conclusions: The additional chemical cleansing with HBX resulted in a statistically significant improvement in bacterial plaque removal during SRP of deep pockets, but it was not effective in reducing calculus deposits.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.342-344
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• 2018

This paper shows the possibility to substitute statistical methods such as mean imputation, correlation coefficient analysis, graph correlation analysis for the proposed algorithm, and replace statistician for processing various abnormal data measured in the water treatment process with it. In addition, this study aims to model a data-filtering system based on a recent fractile pattern and a deep learning-based LSTM algorithm in order to improve the reliability and validation of the algorithm, using the open-sourced libraries such as KERAS, THEANO, TENSORFLOW, etc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.501-503
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• 2017

In the water sector, various data such as flow rate, pressure, water quality and water level are collected during the whole process of water purification plant and piping system. The collected data is stored in each water treatment plant's DB, and the collected data are combined in the regional DB and finally stored in the database server of the head office of the Korea Water Resources Corporation. Various abnormal data can be generated when a measuring instrument measures data or data is communicated over various processes, and it can be classified into missing data and wrong data. The cause of each abnormal data is different. Therefore, there is a difference in the method of detecting the wrong side and the missing side data, but the method of cleansing the data is the same. In this study, a program that can automatically refine missing or wrong data by applying deep learning LSTM (Long Short Term Memory) algorithm will be studied.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.364-373
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• 2022

Purpose: The purpose of this paper is to develope smoke detection using AI model for detecting the initial fire in underground utility tunnels using CCTV Method: To improve detection performance of smoke which is high irregular, a deep learning model for fire detection was trained to optimize smoke detection. Also, several approaches such as dataset cleansing and gradient exploding release were applied to enhance model, and compared with results of those. Result: Results show the proposed approaches can improve the model performance, and the final model has good prediction capability according to several indexes such as mAP. However, the final model has low false negative but high false positive capacities. Conclusion: The present model can apply to smoke detection in underground utility tunnel, fixing the defect by linking between the model and the utility tunnel control system.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.7-17
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• 2020

This study investigated the differences in skin-related habits and skin health behavior according to the gender in college students. The questionnaire surveyed four category on general characteristics, skin-related habits, cosmetic use, and skin care habits. As a result, there were significant differences between gender in smoking(χ²=19.58, p=0.000) and exercise(χ²=17.59, p=0.001) habits. In all categories of cosmetics use, there were significant differences between gender. But both male and female college students were the highest in the 'Not used' and 'Less than once a week" in functional cosmetics, essence, nutritional cream, eye cream, mask pack and deep cleansing items. whereas, there were significant differences between gender in use of sun cream(χ²=31.20, p=0.000). The results of this study showed there were differences in gender according to the survey items. However, female college students were overall better than male college students in skin care.