• Tuberculosis and Respiratory Diseases
• /
• v.43 no.4
• /
• pp.558-570
• /
• 1996

Background : The detection of Collapsible airways has important therapeutic implications in chronic airway disease and bronchial asthma. The distinction of a purely collapsible airways disease from that of asthma is important because the treatment of the dormer may include the use of pursed lip breathing or nasal positive pressure ventilation whereas in the latter, pharmacologic approaches are used. One form of irreversible airflow limitation is collapsible airways, which has been shown to be a Component of asthma or to emphysema, it can be assessed by the volume difference between what exits the lung as determined by a spirometer and the volume compressed as measured by the plethysmography. Method : To investigate whether volume difference between slow and forced vital Capacity(SVC-FVC) by spirometry may be used as a surrogate index of airway collapse, we examined pulmonary function parameters before and after bronchodilator agent inhalation by spirometry and body plethysmography in 20 cases of patients with evidence of airflow limitation(chronic obstructive pulmonary disease 12 cases, stable bronchial asthma 7 cases, combined chronic obstructive pulmonary disease with asthma 1 case) and 20 cases of normal subjects without evidence of airflow limitation referred to the Pusan National University Hospital pulmonary function laboratory from January 1995 to July 1995 prospectively. Results : 1) Average and standard deviation of age, height, weight of patients with airflow limitation was $58.3{\pm}7.24$(yr), $166{\pm}8.0$(cm), $59.0{\pm}9.9$(kg) and those of normal subjects was $56.3{\pm}12.47$(yr), $165.9{\pm}6.9$(cm), $64.4{\pm}10.4$(kg), respectively. The differences of physical characteristics of both group were not significant statistically and male to female ratio was 14:6 in both groups. 2) The difference between slow vital capacity and forced vital capacity was $395{\pm}317ml$ in patients group and $154{\pm}176ml$ in normal group and there was statistically significance between two groups(p<0.05). Sensitivity and specificity were most higher when the cut-off value was 208ml. 3) After bronchodilator inhalation, reversible airway obstructions were shown in 16 cases of patients group, 7 cases of control group(p<0.05) by spirometry or body plethysmography d the differences of slow vital capacity and forced vital capacity in bronchodilator response group and nonresponse group were $300.4{\pm}306ml$, $144.7{\pm}180ml$ and this difference was statistically significant. 4) The difference between slow vital capacity and forced vital capacity before bronchodilator inhalation was correlated with airway resistance before bronchodilator(r=0.307 p=0.05), and the difference between slow vital capacity and forced vital capacity after bronchodilator was correlated with difference between slow vital capacity and forced vital capacity(r=0.559 p=0.0002), thoracic gas volume(r=0.488 p=0.002) before bronchodilator and airway resistance(r=0.583 p=0.0001), thoracic gas volume(r=0.375 p=0.0170) after bronchodilator, respectively. 5) The difference between slow vital capacity and forced vital capacity in smokers and nonsmokers was $257.5{\pm}303ml$, $277.5{\pm}276ml$, respectively and this difference did not reach statistical significance(p>0.05). Conclusion : The difference between slow vital capacity and forced vital capacity by spirometry may be useful for the detection of collapsible airway and may help decision making of therapeutic plans.

• Horticultural Science & Technology
• /
• v.34 no.2
• /
• pp.269-281
• /
• 2016

The statistical analyses of coefficient of variation, heritability, and genetic advance were carried out to identify differences in morphological characteristics, such as the stem and inflorescence length, of 10 major commercial cultivars of spray chrysanthemum (Chrysanthemum morifolium). For morphological characteristics, stem lengths ranged from 46.4 cm to 54.9 cm, the maximum diameter of stem was 5.6 to 8.5 mm, the hardness of the stem was 0.17 to $0.70kg{\cdot}m^{-2}$, the fresh weight of stem was 7.5 to 17.5 g, the dry weight of the stem was 1.6 to 3.3 g, the ratio of dry weight/fresh weight of stem was 15.9% to 23.1%. Also, the number of leaves on the stem was 8.4 to 12.2, the stem leaf area was 17.8 to $37.8m^2$, the fresh weight stem leaves was 5.3 to 18.6 g, the dry weight was 0.5 to 1.4 g and the ratio of dry weight /fresh weight of stem leaves was 7.6% to 11.5%. The inflorescence length ranged from 10.1 to 18.6 cm, the fresh weight of inflorescence was 7.3 to 26.7 g, the dry weight of inflorescence was 1.2 to 2.8 g, the ratio of dry weight /fresh weight of inflorescence was 10.4% to 17.1%. For flower, the diameter of the flower center was 8.2 to 13.3 mm, the petal width was 5.7 to 14.0 mm, the petal length was 12.9 to 33.1 mm, and the petal thickness was 157.8 to $354.4{\mu}m$. The mean values of each character in each cultivar were very different, and DMRT and LSD values based on morphological characteristics among 10 cultivars were highly significant. For variability and genetic parameters, the lowest CV (coefficient of variation), PCV (phenotypic coefficient of variation), and GCV (genotypic coefficient of variation) were 4.79% to 5.15% in stem length, and the highest variations were 62.97% to 65.21% in leaf area. ECV (error or environmental coefficient of variation) was the lowest for leaf area (1.71%) and it was the highest for leaf dry weight (19.30%). Heritability also significantly differed among the characteristics, ranging from 68.69% to 99.67%, the lowest value was shown in ratio of dry weight /fresh weight of stem and the highest value was for leaf area of stem. The value for genetic advance was the lowest in hardness of stem at 0.30 and the highest in leaf thickness at 156.65. The lowest genetic advance as percentage of mean of stem hardness was 9.17%, while the highest percentage of stem length was 134.27%. Thus the characters which had the highest values indicated above show the influence of additive gene action and may provide useful resources for selection programs for agronomic improvement.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.167-181
• /
• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.12 no.2
• /
• pp.156-162
• /
• 2009

Purpose: The aim of this study was to investigate the clinical usefulness of upper gastrointestinal (GI) endoscopy in children with Henoch-Sch$\"{o}$nlein purpura (HSP). Methods: We retrospectively analyzed the clinical, endoscopic, and histopathologic records of children with HSP who had been admitted to the Department of Pediatrics of Gil Hospital and underwent upper GI endoscopy between January 2002 and June 2009. Patients were classified into the following two groups for statistical analysis: duodenal involvement (+) and duodenal involvement (-). Results: Fifty-one children with HSP underwent upper GI endoscopy; the mean age was 7.2${\pm}$2.9 years. The upper GI endoscopy showed abnormalities of the duodenum in 38 cases (74.5%), 22 of which had duodenal ulcers. Among the biopsy specimens obtained from the duodenum of 37 cases, 13 cases (35.1%) had leukocytoclastic vasculitis, neutrophil debri, and/or extravasation of RBCs. Steroid use was more frequent in the duodenal involvement (+) group (86.8%) than the duodenal involvement (-) group (53.8%; p=0.02). The mean length of hospitalization was 13.9${\pm}$8.43 days in the duodenal involvement (+) group and 8.1${\pm}$4.62 days in the duodenal involvement (-) group (p=0.003). The recurrence rate was significantly higher in the duodenal involvement (-) group than the duodenal involvement (+) group (p=0.027), whereas none of the other study parameters, such as the age of onset, renal involvement, and steroid use, led to significantly higher or lower recurrence rates. Conclusion: These results suggest that duodenal involvement can influence the clinical course and prognosis of HSP in children.

• Journal of Intelligence and Information Systems
• /
• v.18 no.3
• /
• pp.185-202
• /
• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Korean Journal of Heritage: History & Science
• /
• v.42 no.1
• /
• pp.122-142
• /
• 2009

This study explored all domestic and international theories for maintenance and health enhancement of an old and big tree, and carried out the anatomical survey of the operation part of the tree toward he current status of domestic surgery and the perception survey of an expert group, and drew out following conclusion through the process of suggesting its reform plan. First, as a result of analyzing the correlation of the 67 subject trees with their ages, growth status. surroundings, it revealed that they were closely related to positional characteristic, damage size, whereas were little related to materials by fillers. Second, the size of the affected part was the most frequent at the bough sheared part under $0.09m^2$, and the hollow size by position(part) was the biggest at 'root + stem' starting from the behind of the main root and stem As a result of analyzing the correlation, the same result was elicited at the group with low correlation. Third, the problem was serious in charging the fillers (especially urethane) in the big hollow or exposed root produced at the behind of the root and stem part, or surface-processing it. The benefit by charging the hollow part was analyzed as not so much. Fourth, the surface-processing of fillers currently used (artificial bark) is mainly 'epoxy+woven fabric+cork', but it is not flexible, so it has brought forth problems of frequent cracks and cracked surface at the joint part with the treetextured part. Fifth, the correlation with the external status of the operated part was very high with the closeness, surface condition, formation of adhesive tissue and internal survey result. Sixth, the most influential thing on flushing by the wrong management of an old and big tree was banking, and a wrong pruning was the source of the ground part damage. In pruning a small bough can easily recover itself from its damage as its formation of adhesive tissue when it is cut by a standard method. Seventh, the parameters affecting the times of related business handling of an old and big tree are 'the need of the conscious reform of the manager and related business'. Eighth, a reform plan in an institutional aspect can include the arrangement of the law and organization of the old and big tree management and preservation at an institutional aspect. This study for preparing a reform plan through the status survey of the designated old and big tree, has a limit inducing a reform plan based on the status survey through individual research, and a weak point suggesting grounds by any statistical data. This can be complemented by subsequent studies.

• Journal of Internet Computing and Services
• /
• v.14 no.5
• /
• pp.69-76
• /
• 2013

The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.

• Tuberculosis and Respiratory Diseases
• /
• v.43 no.3
• /
• pp.308-322
• /
• 1996

Background : Neutrophils are considered to play critical roles in the development of acute respiratory distress syndrome. Histamine, which is distributed abundantly in lung tissue, increases the rolling of neutrophills via increase of P-selectin expression on the surface of endothelial cells and is known to have some interrelationships with IL-1, IL-8 and TNF-$\alpha$. We studied to investigate the effect of the histamine on the acute lung injury of the rats induced by intratracheal insufflation of TNF-$\alpha$ which has less potency to cause lung injury compared to IL-1 in rats. Methods : We intratracheally instilled saline or TNF(R&D, 500ng), IL-1(R&D, 50ng)or histamine of varius dose(1.1, 11 and $55\;{\mu}g/kg$) with and without TNF separately in Sprague-Dawley rats weighing 270-370 grams. We also intratracheally treated IL-1(50ng) along with histamine($55\;{\mu}g/kg$). In cases, there were synergistic effects induced by histamine on the parameters of TNF-induced acute lung injury, antihistamines(Sigma, mepyramine as a $H_1$ receptor blockade and ranitidine as a $H_2$ receptor blockade, 10 mg/kg in each)were co-administered intravenously to the rats treated TNF along with histamine($1.1\;{\mu}g/kg$) intratraeheally. Then after 5 h we measured lung lavage neutrophil numbers, lavage cytokine-induced neutrophil chemoattractants(CINC), lung myeloperoxidase activity(MPO) and lung leak. We also intratracheally insufflated TNF with/without histamine($11\;{\mu}g/kg$), then after 24 h measured lung leak in rats. Statistical analyses were done by Kruskal-Wallis nonparametric ANOVA test with Dunn's multiple comparison test or by Mann-Whitney U test. Results : We found that rats given TNF, histamine alone(11 and $55\;{\mu}g/kg$), and TNF with histamine(l.1, 11, and $55\;{\mu}g/kg$) intratracheally had increased (p<0.05) lung MPO activity compared with saline-treated control rats. TNF with histamine $11\;{\mu}g/kg$ had increased MPO activity (P=0.0251) compared with TNF-treated rats. TNF and TNF with histamine(1.1, 11, and $55\;{\mu}g/kg$) intratracheally had all increased (p<0.05) lung leak, lavage neutophil numbers and lavage CINC activities compared with saline. TNF with histamine $1.1\;{\mu}g/kg$ had increased (p=0.0367) lavage neutrophil numbers compared with TNF treated rats. But there were no additive effect of histamine with TNF compared with TNF alone in acute lung leak on 5 h and 24 h in rats. Treatment of rats with the $H_1$ and $H_2$ antagonists resulted in inhibitions of lavage neutrophil accumulations and lavage CINC activity elevations elicited by co-treated histamine in TNF-induced acute lung injury intratracheally in rats. We also found that rats given IL-1 along with histamine intratracheally did not have increase in lung leak compared with IL-1 treated rats. Conclusion : Histamine administered intratracheally did not have synergistic effects on TNF-induced acute lung leak inspite of additive effects on increase in MPO activity and lavage neutrophil numbers in rats. These observations suggest that instilling histamine intratracheally would not play synergistic roles in neutrophil-mediated acute lung injury in rats.