• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.329-337
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• 2015

Fluorine is an element that promotes dental caries preventive effect at proper concentration level, but may have significantly undesirable impact on dental caries if correct information of fluorine is not recognized. Therefore, this study is intended to evaluate fluorine awareness among the adults in their 20s and analyze oral health conditions based on such awareness in order to promote dental caries preventive effect and convey correct knowledge of fluorine. For this study, 82 adult men and 102 adult women in their 20s, all of whom lived in Daegu, Gyeongsangbuk-do, were surveyed from May 1, 2014 to June 30 of the same year, along with oral health examination to evaluate their oral health conditions. The results of the analysis showed that 19.46% of respondents answered "No" to the question related to awareness towards the use of fluorine in DT rate dental clinic based on the degree of fluorine awareness, which was higher than 11.10% who answered "Yes"(p<.05). Moreover, 18.32%of respondents answered "No" to the question related to whether they were checking the label indicating the fluorine content in product, which was the highest rate, suggesting that the rate was higher when there was stronger tendency to avoid reading the precaution notice (p<.05). In addition, 71.98% of respondents answered "Yes" to the question associated with the awareness to the use of fluorine in FT rate dental clinic based on the fluorine awareness, which was higher than 49.04% of respondents who answered "No" (p<.05). This study is meaningful in that it provides basic data for the implementation of fluorine prevention projects in the period ahead by determining the effect of fluorine awareness on oral health conditions. Based on aforesaid results, both educational and promotional activities need to be carried out vigorously to help raise fluorine awareness.

• Journal of Korea Game Society
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• v.10 no.1
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• pp.65-78
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• 2010

In Video Software Dealers Association v. Arnold Schwarzenegger, the federal 9th Circuit Court decided that a California law imposing restrictions and a labeling requirement on the sale or rental of violent video games to minors (the "Act") violated rights guaranteed by the First and Fourteenth Amendments to the United States Constitution because: (1) the state introduced insufficient evidence to support a compelling interest that video games created psychological or neurological harm, (2) the Act was not the least-restrictive alternative to negate the harm, and (3) the lower, rational basis standard applicable to commercial speech did not apply to the Act's labeling requirements because the required label did not convey factual information. On the contrary, Korean Constitutional Court decided that "Harmful Medium to Youth" and "Preliminary Rate Classification" would be constitutional. However, under the least-restrictive method rule of the U. S. Court and Korean Court, overlap application of "Harmful Medium to Youth" and "Preliminary Rate Classification" could be a problem and it would be possible that stronger regulation among these would be found as unconstitutional.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.767-776
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• 2008

Microarray technology is extensively being used in experimental molecular biology field. Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification of many diseases. One of the two major problems in microarray data classification is that the number of genes exceeds the number of tissue samples. The other problem is that current methods generate classifiers that are accurate but difficult to interpret. Our paper addresses these two problems. We performed a direct integration of individual microarrays with same biological objectives by transforming an expression value into a rank value within a sample and generated rank-comparison decision rules with variable number of genes for cancer classification. Our classifier is an ensemble method which has k top scoring decision rules. Each rule contains a number of genes, a relationship among involved genes, and a class label. Current classifiers which are also ensemble methods consist of k top scoring decision rules. However these classifiers fix the number of genes in each rule as a pair or a triple. In this paper we generalized the number of genes involved in each rule. The number of genes in each rule is in the range of 2 to N respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Also our classifier is readily interpretable, accurate with small number of genes, and shed a possibility of the use in a clinical setting.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.90-100
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• 2017

The purpose of this study is to detect and label the vehicles using the drone images as a way to overcome the limitation of the existing point and section detection system and vehicle gap estimation on Arterial road. In order to select the appropriate time zone, position, and altitude for the acquisition of the drone image data, the final image data was acquired by shooting under various conditions. The vehicle was detected by applying mixed Gaussian, image binarization and morphology among various image analysis techniques, and the vehicle was labeled by applying Kalman filter. As a result of the labeling rate analysis, it was confirmed that the vehicle labeling rate is 65% by detecting 185 out of 285 vehicles. The gap was calculated by pixel unitization, and the results were verified through comparison and analysis with Daum maps. As a result, the gap error was less than 5m and the mean error was 1.67m with the preceding vehicle and 1.1m with the following vehicle. The gaps estimated in this study can be used as the density of the urban roads and the criteria for judging the service level.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.40-53
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• 2018

In smart card data, movement of railway passengers appears in order of smart card terminal ID. The initial terminal ID holds information on the entering station's tag-in railway line, the final terminal ID the exit station tag-out railway line, and the middle terminal ID the transfer station tag subway line. During the past, when the metropolitan city rail consisted of three public corporations (Seoul Metro, Incheon Transit Corporation, and Korail), OD data was expressed in two metrics of initial and final smart card terminal ID. Recently, with the entrance of private corporations like Shinbundang Railroad Corporation, and UI Corporation, inclusion of entering transfer line terminal ID and exiting transfer line terminal ID as part of Chain OD has become standard. Exact route construction using Chain OD has thus become integral as basic data for revenue allocation amongst metropolitan railway transport corporations. Accordingly, path detection in railway networks has evolved to an optimal path detection problem using Chain OD, hence calling for a renewed solution method. This research proposes an optimal path detection method between the initial terminal ID and final terminal ID of Chain OD terminal IDs within the railway network. Here, private line transfer TagIn/Out must be reflected in optimal path detection using Chain OD. To achieve this, three types of link-based optimum path detection methods are applied in order of 1. node-link, 2. link-link, 3. link-node. The method proposed based on additional path costs is shown to satisfy the optimal conditions.

• Journal of Korean Academy of Oral Health
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• v.42 no.4
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• pp.199-203
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• 2018

Objectives: The market for bottled water is increasing steadily in South Korea. Bottled water contains several naturally occuring minerals, such as calcium, magnesium, sodium, and fluoride. Fluoride is proven to be effective in preventing dental caries. In South Korea, the maximum permissible concentration of fluoride is 2 ppm for bottled water and 1.5 ppm for tap water. The aim of this study was to investigate the fluoride content of different commercially available brands of bottled water in South Korea, and compare the measured fluoride concentration to the concentration written on the label of each brand of bottled water. Methods: Twenty-seven of the 59 different brands of bottled water produced in South Korea were investigated in this study. Three bottles of each brand were purchased from supermarkets, marts, and convenience stores in each region of Korea in August 2016. For each bottled water brand, the fluoride content was measured three times using a fluoride-ion selective electrode (Orion ionplus Fluoride Electrode 9609, Orion Research, USA). The calibration curve was generated using 0.2 and 2 ppm standard solutions, and confirmed using a 1 ppm standard solution. Results: The mean fluoride content of the 27 brands of bottled water was $0.374{\pm}0.332mg/L$ (range=0.040 to 1.172 mg/L). The fluoride content was labeled by the manufacturer, on each of the tested brands of bottled water. In eight brands, the labeled fluoride content differed from the experimental data. The minimum to maximum fluoride content measured from 10 brands showed a variation of 0.3 mg/L or more when compared to the labeled fluoride content. Conclusions: This study investigated the fluoride content of various brands of bottled water produced in South Korea and compared the measured fluoride levels with fluoride information on the bottle labels. To ensure that consumers are suitably informed regarding their exposure to fluoride, correct labelling of fluoride content in bottled water is important.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.139-147
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• 2021

This paper introduces and implements a Sound Event Detection (SED) system based on weakly-supervised learning where only part of the data is labeled, and analyzes the effect of parameters. The SED system estimates the classes and onset/offset times of events in the acoustic signal. In order to train the model, all information on the event class and onset/offset times must be provided. Unfortunately, the onset/offset times are hard to be labeled exactly. Therefore, in the weakly-supervised task, the SED model is trained by "strongly labeled data" including the event class and activations, "weakly labeled data" including the event class, and "unlabeled data" without any label. Recently, the SED systems using the mean-teacher model are widely used for the task with several parameters. These parameters should be chosen carefully because they may affect the performance. In this paper, performance analysis was performed on parameters, such as the feature, moving average parameter, weight of the consistency cost function, ramp-up length, and maximum learning rate, using the data of DCASE 2020 Task 4. Effects and the optimal values of the parameters were discussed.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Herbal Formula Science
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• v.30 no.4
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• pp.241-248
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• 2022

Objective : The purpose of this trial is to observe the preliminary effects of Salvia plebeia (SP) extract on quality of life in patients with solid cancer. Methods : This is a prospective, open-label, single-arm, and single-dose clinical trial. Twenty participants who have been diagnosed with solid cancer between the ages of 20 and 65 will be included. All participants will be administered SP granules for 12 weeks. Data will be collected at 4, 8, and 12 weeks after enrollment. The primary outcome is quality of life, using the Korean version of the Functional Assessment Cancer Therapy-General questionnaire. Secondary outcomes include tumor markers in blood tests for each cancer type, soluble programmed death-ligand 1, the percentage of natural killer cells among lymphocytes, ratio of T-helper and T-suppressor cells, ratio of total T, T-helper, T-suppressor, and B cells in lymphocytes, level of C-reactive protein, and tumor size via radiology examination. Safety will be assessed by clinical laboratory tests and monitoring of adverse events. Discussion : This study aims to observe the effects of an oral administration of SP preparations in patients with solid cancer on changes in quality of life and an improvement in immune function. It is expected to provide objective evidence of the effect and safety of SP for patients with solid cancer. Trial registration: KCT0007315 (Clinical Research Information Service)

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.159-172
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• 2023

In general, social problem-solving research aims to create important social value by offering meaningful answers to various social pending issues using scientific technologies. Not surprisingly, however, although numerous and extensive research attempts have been made to alleviate the social problems and issues in nation-wide, we still have many important social challenges and works to be done. In order to facilitate the entire process of the social problem-solving research and maximize its efficacy, it is vital to clearly identify and grasp the important and pressing problems to be focused upon. It is understandable for the problem discovery step to be drastically improved if current social issues can be automatically identified from existing R&D resources such as technical reports and articles. This paper introduces a comprehensive dataset which is essential to build a machine learning model for automatically detecting the social problems and solutions in various national research reports. Initially, we collected a total of 700 research reports regarding social problems and issues. Through intensive annotation process, we built totally 24,022 sentences each of which possesses its own category or label closely related to social problem-solving such as problems, purposes, solutions, effects and so on. Furthermore, we implemented four sentence classification models based on various neural language models and conducted a series of performance experiments using our dataset. As a result of the experiment, the model fine-tuned to the KLUE-BERT pre-trained language model showed the best performance with an accuracy of 75.853% and an F1 score of 63.503%.