• Restorative Dentistry and Endodontics
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• v.35 no.6
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• pp.486-491
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• 2010

Objectives: This study examined the effect of 2% chlorhexidine on the ${\mu}TBS$ of a direct composite restoration using one-step self-etch adhesives on human dentin. Materials and Methods: Twenty-four extracted permanent molars were used. The teeth were assigned randomly to six groups (n = 10), according to the adhesive system and application of chlorhexidine. With or without the application of chlorhexidine, each adhesive system was applied to the dentin surface. After the bonding procedure, light-cure composite resin buildups were produced. The restored teeth were stored in distilled water at room temperature for 24 hours, and then cut and glued to the jig of the microtensile testing machine. A tensile load was applied until the specimen failed. The failure mode was examined using an operating microscope. The data was analyzed statistically using one-way ANOVA, Student's t-test (p < 0.05) and Scheffet's test. Results: Regardless of the application of chlorhexidine, the Clearfil $S^3$ Bond showed the highest ${\mu}TBS$, followed by G-Bond and Xeno V. Adhesive failure was the main failure mode of the dentin bonding agents tested with some samples showing cohesive failure. Conclusions: The application of 2% chlorhexidine did not affect the ${\mu}TBS$ of the resin composite to the dentin using a one-step self-etch adhesive.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.706-714
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• 2012

The Korea Meteorological Administration provided the recent four-years records of weather dataset for our very short-term heavy rainfall prediction. We divided the dataset into three parts: train, validation and test set. Through feature selection, we select only important features among 72 features to avoid significant increase of solution space that arises when growing exponentially with the dimensionality. We used a differential evolution algorithm and two classifiers as the fitness function of evolutionary computation to select more accurate feature subset. One of the classifiers is Support Vector Machine (SVM) that shows high performance, and the other is k-Nearest Neighbor (k-NN) that is fast in general. The test results of SVM were more prominent than those of k-NN in our experiments. Also we processed the weather data using undersampling and normalization techniques. The test results of our differential evolution algorithm performed about five times better than those using all features and about 1.36 times better than those using a genetic algorithm, which is the best known. Running times when using a genetic algorithm were about twenty times longer than those when using a differential evolution algorithm.

• Women's Health Nursing
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• v.6 no.1
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• pp.117-128
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• 2000

The purpose of this study was to examine the effect of low back pain relieving program on back muscle strength, intensity of pain, low back disability level in elementary school teachers who have low back pain. subjects were elementary school women teachers who worked at eight elementary school located in Seoul. Intended subjects size were seventy consist of thirty-four experimental group(three schools) and thirty-six control group(five schools), but actual subjects size was forty-four. Among the forty-four patients subjects, twenty-three were experimental group receiving health education about right postures, etiologies of low back pain, diagnosis of low back pain and exercise program composed of muscle strengthening exercise, stretching exercises and twenty-one were control group. During the 8 weeks program, the subjects were received two times education and six times group exercise practices in 1st week and three times per week group exercise practices, two times education in other 7 weeks. This study as carried out from April 1, 1999 to June 30, 1999. Back muscle strength was measured by back muscle strength measuring machine and the intensity of pain were measured by the Visual Analogue Scale(VAS), and level of disability was measured by Oswestry low back pain disability scale. Study measurements were taken before and after 8 week exercise program. Data were analyzed using paired t-test, and ANCOVA. The results were summarized as follows. 1. After low back relieving program, back muscle strength was increased significantly(p=0.000) and there was significant difference in back muscle strength change between experimental group and control group(p=0.002). 2. After low back pain relieving program, pain on anterior bending, pain on posterior bending were decreased significantly than measurements before the program(p=0.000 p=0.000) and there was significant difference in pain on anterior bending and posterior bending change between experimental group and control group(p=0.000, p=0.000). 3. After low back pain relieving program, Oswestry disability scale scores were decreased significantly(p=0.000, p=0.000) but there was no significant difference in Oswestry disability score change between experimental group and control group.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.145-154
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• 2009

High spatial resolution satellite image classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, the extraction of the spatial information is one of the most important steps in high resolution satellite image classification. This study proposes a new spatial feature extraction method, named SSI(Shape-Size Index). SSI uses a simple region-growing based image segmentation and allocates spatial property value in each segment. The extracted feature is integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a SVM(Support Vector Machines) classifier. In order to evaluate the proposed feature extraction method, KOMPSAT-2 and QuickBird-2 data are used for experiments. It is demonstrated that proposed SSI algorithm leads to a notable increase in classification accuracy.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.3
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• pp.322-335
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• 2019

Objectives: The goal of this study is to standardize industry, process, and job within work environment measurement information. Methods: We selected 180 work environment measurement reports on 30 industries from a database monitored from 2014 to 2016 by the Korea Industrial Health Association. Ten industrial hygienists, each with over five years of experience in measurement, conducted a primary standardization of 180 reports. Two professional industrial hygienists with more than 20 years of experience each reviewed and revised the results of the primary standardization. We also examined the validity on the usefulness of the standardized database by the two industrial hygienists. Results: The final standardization results were classified into eight major categories, 23 sub-major categories, 39 minor categories, 53 unit categories and 70 sub-unit categories in the Korean Standard Industrial Classification (KSIC) 10th revision. A total of 161 processes were standardized, and there were 148 processes with K2B codes. Standard job was coded into 13 job groups including operator, automobile maintenance, nurse, maintenance, manager, excavating machine operator, forklift driver, radiologist, clinical pathologist, signer, researcher, kitchen assistant, and concrete reinforcement ironworker. Conclusions: Although the standardized information in this study may be only a part of the total information, it can be useful for improvement of the K2B system. Additional research is needed for an ongoing clean-up of data in the K2B and re-calibration and reclassification of standard processes until the future national exposure monitoring system is fully established.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.115-123
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• 2019

This is descriptive study to 2nd analysis data KNHANES IV-VI about risk factors of readmission among patients with cardiovascular disease. Among the total 65,973 adults, 1,037 with angina or myocardial infarction were analyzed. The analysis was conducted using SPSS window 21 Program and CHAID decision tree was used in the classification analysis. Root nodes are economic activity(χ²=12.063, p=.001), children's nodes are personal income(χ²=6.575, p=.031), weight change(χ²=12.758, p=.001), residential area(χ²=4.025, p=.045), direct smoking(χ²=3.884, p=.031). p=.049), level of education(χ²=9.630, p=.024). Terminal nodes are hypertension(χ²=3.854, p=.050), diabetes mellitus(χ²=6.056, p=.014), occupation type(χ²=7.799, p=.037). We suggest that the development and operation of programs considering the integrated approach of various factors is necessary for the readmission management of cardiovascular patients.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.29-35
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• 2020

In this paper, we propose a new structured entity recognition DeNERT model. Recently, the field of natural language processing has been actively researched using pre-trained language representation models with a large amount of corpus. In particular, the named entity recognition, which is one of the fields of natural language processing, uses a supervised learning method, which requires a large amount of training dataset and computation. Reinforcement learning is a method that learns through trial and error experience without initial data and is closer to the process of human learning than other machine learning methodologies and is not much applied to the field of natural language processing yet. It is often used in simulation environments such as Atari games and AlphaGo. BERT is a general-purpose language model developed by Google that is pre-trained on large corpus and computational quantities. Recently, it is a language model that shows high performance in the field of natural language processing research and shows high accuracy in many downstream tasks of natural language processing. In this paper, we propose a new named entity recognition DeNERT model using two deep learning models, DQN and BERT. The proposed model is trained by creating a learning environment of reinforcement learning model based on language expression which is the advantage of the general language model. The DeNERT model trained in this way is a faster inference time and higher performance model with a small amount of training dataset. Also, we validate the performance of our model's named entity recognition performance through experiments.

• Journal of the Korean Regional Science Association
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• v.37 no.3
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• pp.3-18
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• 2021

The fourth Industrial Revolution is transforming the industrial structure of the region, and it is necessary to develop new industries and technologies that reflect regional characteristics. The purpose of this study is to measure the knowledge relatedness and technological complexity in Busan, Ulsan, and Gyeongnam, and to identify technologies with potential for regional industrial differentiation strategies. Using patent data from 2015 to 2019, co-occurrence matrices were derived from 652 IPC codes, and the knowledge relatedness density and technology complexity index were calculated. Network analysis was performed using the knowledge relatedness density. As a result of analysis, it was found that mechanical engineering occupied a large proportion, followed by chemistry and electrical engineering. As a result of applying the risk-benefit framework to derive technologies with the potential to differentiate local industries, the technological capabilities of low-risk-high-benefit were different. Among mechanical engineering, technologies such as engine, machine operation, and transportation were included in Busan. In Ulsan, environmental technology in chemical and materials, and heat treatment technology in mechanical engineering were technologies with low-risk and high-benefit capabilities. Gyeongnam showed competence in mechanical engineering, chemistry, and electrical engineering in some areas such as Gimhae, Yangsan, and Changwon. The results of this study are meaningful in that they identified technologies with potential for selecting and deriving strategic industries for regional growth based on latent knowledge in the region.

• Journal of vocational education research
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• v.29 no.3
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• pp.61-83
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• 2010

The purpose of this study was to analyze high schools' general and special subject required to successfully complete same stream curriculum which is identical to their major from agricultural high school, and to offer basic data on strategies of organizing agricultural high schools' curriculum for improving universities' major basic competencies. Using purposeful sampling technique, the professors of 116 universities professors in 8 agricultural university were analyzed through the survey research. The result was as follows. first, it appeared that for successful completion of major subjects of the same stream university, the basic science subject such as biology and chemistry has high relation with major basic ability, however math and physics are related highly in agricultural machine and agricultural civil engineering department, economics and math are in agricultural produce distribution department. Second, the basic ability such as linguistic competence and foreign language ability are essential to complete major subject. Third, if we look into relation of agriculture and life science industry stream specialized subject with major basic competencies, we can find considerable similarity between major field of university and subject name of specialized high school. Fourth, the main opinion is that basic concept and principle, laws of nature are should be main contents which is able to be practical, however experiment and practice is in food processing department, and academic theory is in biotechnology department.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.