• Journal of Korean society of Dental Hygiene
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• v.7 no.2
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• pp.135-151
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• 2007

The purpose of this study was to examine the work environments of dental hygienists, to find out about what problems there were with their work environments and ultimately to help improve their work environments. It's basically intended to pave the way for furthering the welfare and interests of dental hygienists. The subjects in this study were dental hygienists who were selected by random sampling from among the members of Korean Dental Hygienists Association. Approximately 20 percent of the members each were selected from every region across the nation, and their work environments were investigated in consideration of the kind of their workplaces, service area, career and field of duties. As for the demographic characteristics of the dental hygienists investigated, there were differences between those who worked in the field of health care and the clinical workers. More of the former were older and married, and the former was ahead of the latter in career and education as well. Regarding working hours and leave of absence by kind of workplace, the number of regular average holidays was different according to their place of employment. Dental hospitals(6.66 days) and dental clinics(6.81 days) gave their employees less days off on the whole, whereas public dental clinics(19.29 days) granted the dental hygienists the longest leave of absence. Also, there was a broad gap in the number of regular average holidays among different regions in the nation. The dental hygienists who worked in Gangweon province enjoyed the longest holidays(10.88 days), while those on Jeju Island took the shortest vacation(4.46 days). Concerning monthly mean pay by place of employment, those who worked in public dental clinics were paid the best, and the dental hospital employees received the smallest pay. Their monthly mean pay significantly varied with the kind of their workplaces. As to connections between service area and pay level in the event of the dental hygienists with a four-year career, those who served in Seoul were paid the best(1,820,800 won), followed by Gyeonggi province(1,795,800 won), Gyeongsang province(1,604,200 won), metropolitan cities(1,424,800), Gangweon province(1,300,000 won) and Jeolla province(1,016,700 won). In regard to the starting pay in the different areas, the starting pay was largest in Seoul(1,501,800 won) and smallest in Jeolla province(904,000 won). Concerning work environments by place of employment, the dental hygienists in public dental clinics, general hospitals and university hospitals were far older than the others, and the career of the former was much larger than that of the latter. As to the number of regular leave of absence, public dental clinics, general hospitals and university hospitals were different from dental hospitals and clinics in that regard as well. Concerning monthly pay, public dental clinics paid their employees the best, and dental hospitals and clinics were ahead in terms of pay raise. But the reason seemed that public dental clinics and general hospitals increased the pay of their employees based on a fixed wage system and according to a fixed rate at the same time. As for relations between career and work environments, the pay of the dental hygienists differed with their career. The amount and rate of pay raise were largest for those whose career was between four years and less than six years, and smallest for those whose career was between seven years and less than nine years. The above-mentioned findings of the study suggested that in order to give dental hygienists better treatment, pay and welfare benefits should urgently be improved, and that it's required to take actions to boost their job satisfaction. Besides, they should be given more chances to receive education or to take training courses in pursuit of self-development, and how to narrow gaps in work environments among different regions or fields should carefully be considered.

• Clinical and Experimental Pediatrics
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• v.52 no.8
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• pp.944-952
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• 2009

Purpose : To investigate the effects of angiotensin II inhibition on the epithelial to mesenchymal transition (EMT) in the developing kidney, we tested the expression of EMT markers and nestin in angiotensin converting enzyme (ACE) inhibitor-treated kidneys. Methods : Newborn rat pups were treated with enalapril (30 mg/kg/d) or a vehicle for 7 days. Immunohistochemistry for the expression of ${\alpha}$-smooth muscle actin (SMA), E-cadherin, vimentin, and nestin were performed. The number of positively-stained cells was determined under 100 magnification in 10 random fields. Results : In the enalapril-treated group, ${\alpha}SMA-positive$ cells were strongly expressed in the dilated tubular epithelial cells. The number of ${\alpha}SMA-positive$ cells in the enalapril-treated group increased in both the renal cortex and medulla, compared to the control group (P<0.05). The expression of E-cadherin-positive cells was dramatically reduced in the cortical and medullary tubular epithelial cells in the enalapril-treated group (P<0.05). The number of vimentin- and nestin-positive cells in the cortex was not different in comparisons between the two groups; however, their expression increased in the medullary tubulointerstitial cells in the enalapril-treated group (P<0.05). Conclusion : Our results show that ACE inhibition in the developing kidney increases the renal EMT by up-regulating ${\alpha}SMA$ and down-regulating E-cadherin. Enalapril treatment was associated with increased expression of vimentin and nestin in the renal medulla, suggesting that renal medullary changes during the EMT might be more prominent, and ACE inhibition might differentially modulate the expression of EMT markers in the developing rat kidney.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.161-173
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• 2020

As ICT convergence technology has spread and applied to various industrial fields and society in general, interest in rural entrepreneurship using smart farm as a means for solving many pending problems in agriculture is increasing. In this context, this study is to look at the influential factors in terms of perceived usefulness associated with the rural entrepreneurial intention using smart farm and suggest a proposal for spreading smart farms. The subjects were 296 general adults over 20 years old who were selected by simple random sampling method. The research method was exploratory factor analysis and multiple regression analysis using IBM SPSS 22.0. The perceived usefulness of smart farm, which are availability, reliability and economic efficiency were selected as independent variables to analyze the influential factors on rural entrepreneurial intention using smart farm and the moderating effect of personal innovation was observed. As a result, reliability and economic efficiency have a positive(+) influence on rural entrepreneurial intention using smart farm. And personal innovation moderates the relationship between the availability, reliability of smart farm and rural entrepreneurial intention using smart farm. The results of this study have significance in that we devised and empirically revealed factors affecting rural entrepreneurship intentions from the perspective of perceived usefulness of smart farms, away from studies of general entrepreneurship intention factors such as internal personal characteristics and external environmental factors. The implications of the study are expected to be utilized at the seeking direction of policy for potential entrepreneur using smart farm, the training and consulting in actual field of smart farm.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.12 s.159
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• pp.1690-1696
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• 2006

The purpose of this study is to provide the basic data useful in designing apparel patterns for aged abdominal obese women. The body measurements of 318 women were taken at random, whose ages were over 60 and fields of action were colleges, sports centers, or business sites in Seoul and the neighboring districts. A total of 33 features in the upper body and lower body were used fer the anthropometric measurement and analysis using anthropometry. The collected measurement data were processed statistically using the SPSS 12.0 program for technical statistical analysis, t-test, frequency analysis, correlation analysis. The results of the study are as follows. 1. Subjects were classified into two groups as a result of analysis for measurement data. It was revealed that 251(about 79 percent) women of total subjects(n=318) have a characteristic of abdominal obese body type and elderly women of these group usually had big abdomen rather than hip. The criteria of abdominal obesity based on waist-hip ratio, WHR(=0.85). 2. Aged abdominal obese women have shown much larger size in most body measurements except items of some vertical length, such as bust ponit-bust point, font interscye, back interscye with circumference and depth of armscye, bust, waist, abdomen and hip while showing no difference in height, biacrominal breadth, hip width, neck shoulder point to breast point, crotch length. 3. Vervaeck index(=100.1) and Rohrer index(=1.7) indicated that the abdominal obese women were fat in overall body. And aspect ratio of waist(=0.86), abdomen(=0.92) and hip(=0.75) also appeared high that the shape of cross sections in those regions was similar to a figure of circle 4. In view of the correlation coefficient between hip circumference and the rest measurement items, and between hip circumference inclusively of the abdomen protrusion and the rest measurement items, there were found some differences for each group. In case of Group (abdominal obese group), the former is smaller than the other. 5. In case of Abdominal obese women, hip circumference inclusively of the abdomen protrusion is more mutually related to the rest items related to make apparel pattern as waist circumference, depth of armscye and so on than what hip circumference is. This result indicated which must be considered hip circumference inclusively of the abdomen protrusion to make apparel patterns for abdominal obese women unlike women of common body types.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Sasang Constitutional Medicine
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• v.12 no.1
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• pp.24-36
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• 2000

Purpose : Many believe that the Sasang mentioned in the I Ching and the Sasang of Sasang Medicine (as expounded by Dr. Lee Je-ma in the book Longevity and Life Preservation in Oriental Medicine) refer to different concepts. This is untrue. In order to understand the thought patterns of Dr. Lee Je-ma and his book, it is necessary to first understand the concepts of the I Ching. The I Ching was the most respected text in Dr. Lee Je-ma time, and served as the foundation upon which his medicine stood. The purpose of this research is to understand the concept of Sasang in the I Ching and how it applies to the Sasang medicine. Method : The authors first defined the term Sasang according to the theory of I Ching. It was then discussed in relation to theories of modern science. Inferences were made as to how Sasang corresponds to the terminologies and concepts of modern science. The characteristics of Sasang interpreted through modern science were then applied to the physiology, pathology and pharmacology of Sasang Medicine. Results and Conclusion : 1. The Sasang theory of the I Ching organizes seemingly random and isolated natural phenomena into four distinct groups according to various attributes. The particular characteristics representing each of these four categories are known as Sasang. 2. The Sasang theory of I Ching has a strong correlation to the Theory of Relativity and the Theory of Complementarity, as well as the Digital and Fractal Theories. 3. By applying the Sasang Theory to various fields, the seemingly unrelated principles of physics, chemistry, biology and medicine can be seen as parts of a whole. 4. Sasang Medicine categorizes human morphology, physiology and pharmacology into four categories according to the characteristics defined by the Sasang Theory of the I Ching. 5. Grouping new discoveries of modern physics, chemistry, biology and medicine according to the Sasang Theory will bring to light the intricacies of the Sasang Theory while facilitating the incorporation of modern science into Sasang Medicine.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• Journal of Physiology & Pathology in Korean Medicine
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• v.26 no.4
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• pp.551-556
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• 2012

The database for the oriental medicine had been existed in documentation in past times and it has been developed to the database type for random accesses in the information society. However, the aspects of the database are not so diversified and the database for the bio herbal material exists in widened type dictionary style. It is a situation that the database which handles the in-depth raw herbal medicines is not sufficient in its quantity and quality. Korean wild grape is a deciduous plant categorized into the Vitaceae and it was found experimentally that it has various medical effects. It is one of the medical materials with higher potentiality of academic study and commercialization recently because it has a bigger possibility to be applied into diverse industrial fields including the medical product for health, food and beauty. We constituted the cooperative system among the Muju cluster business group for Korean mountain wild grapes, Physiology Laboratory in Kyung Hee University Oriental Medicine and Medical Classics Laboratory in Kyung Hee University Oriental Medicine with a view to focusing on such potentiality and a database for Korean wild grapes was made a touchstone for establishing the in-depth database for the single bio medical materials. First of all, the literatures based on the North East Asia in ancient times had been categorized into the classical literature (Korean literature published by government organization, Korean classical literature, Chinese classical literature and classical literature fro Korean and Chinese oriental medicine) and modern literature (Modern literature for oriental medicine, modern literature for domestic and foreign herbal medicine) to cover the eastern and western research records and writings related to Korean wild grapes and the text-mining work has been performed through the cooperation system with the Medical Classics Laboratory in Kyung Hee University Oriental Medicine. First of all, the data for the experiment and theory for Korean wild grape were collected for the Medline database controlled by the Parliament Library of USA to arrange the domestic and foreign theses with topic for Korean wild grapes and the network hyperlink function and down load function were mounted for self-thesis searching function and active view based on the collected data. The thesis searching function provides various auxiliary functions and the searching is available according to the diverse searching/queries such as the name of sub species of Korean wild grape, the logical intersection index for the active ingredients, efficacy and elements. It was constituted for the researchers who design the Korean wild grape study to design of easier experiment. In addition, the data related to the patents for Korean wild grape which were collected from European Patent Office in response to the commercialization possibility and the system available for searching and view was established in the same viewpoint. Perl was used for the query programming and MS-SQL for database establishment and management in the designing of this database. Currently, the data is available for free use and the address is as follows. http://163.180.41.43:8011/index.html

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Journal of Chest Surgery
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• v.40 no.1 s.270
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• pp.8-16
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• 2007

Background: We performed a prospective clinical study to evaluate the ultrastructural integrity of the myocardium after using Histidine-Tryptophan-Ketoglutarate (HTK) solution in comparison with blood cardioplegic solution during congenital heart surgery. Material and Method: Twenty two patients with acyanotic heart disease, who were scheduled for elective open heart surgery, were randomized into two groups. The HTK Group (n=11) received HTK cardioplegic solution; the blood group (n=11) received conventional blood cardioplegic solution during surgery. The preoperative diagnoses included ventricular septal defect (n=9) and atrial septal defect (n=2) in each group. A small biopsy specimen was taken from the right ventricle's myocardium, and this was processed for ultrastructural examination at the end of 30 minutes of reperfusion. Semiquantitative electron microscopy was carried out 'blindly' in 4 areas per specimen and in 5 test fields per area by 'random systematic sampling' and 'point and intersection counting'. The morphology of the mitochondrial membrane and cristae were then scored. The interstitial edema of the myocardium was also graded. Result: The semiquantitative score of the mitochondrial morphology was $19.65{\pm}4.75$ in the blood group and $25.25{\pm}5.85$ in the HTK group (p=0.03). 6 patients (54.5%) in the blood group and 3 patients (27.3%) in the HTK group were grade 3 or more for the interstitial edema of the myocardium. Conclusion: The ultrastructural integrity was preserved even better with HTK solution than with conventional blood cardioplegic solution.