• Tuberculosis and Respiratory Diseases
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• v.56 no.1
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• pp.51-66
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• 2004

Objectives : The matrix metalloproteinases (MMPs) that participate in the extracellular matrix metabolism play a important role in the progression of pulmonary fibrosis. The effects of the MMPs are regulated by several factors including Th-1 cytokines, $interferon-{\gamma}$ ($IFN-{\gamma}$). Up to now, $IFN-{\gamma}$ is known to inhibit pulmonary fibrosis, but little is known regarding the exact effect of $IFN-{\gamma}$ on the regulation of the MMPs. This study investigated the effects of $interferon-{\gamma}$ on the pulmonary fibrosis and the expression of the lung MMP-2,-9, TIMP-1,-2, and Th-2 cytokines in aa rat model of bleomycin induced pulmonary fibrosis. Materials and methods : Male, specific pathogen-free Sprague-Dawley rats were subjected to an intratracheal bleomycin instillation. The rats were randomized to a saline control, a bleomycin treated, and a bleomycin+$IFN-{\gamma}$ treated group. The bleomycin+$IFN-{\gamma}$ treated group was subjected to an intramuscular injection of $IFN-{\gamma}$ for 14 days. At 3, 7, 14, and 28 days after the bleomycin instillation, the rats were sacrificed and the lungs were harvested. In order to evaluate the effects of the $IFN-{\gamma}$ on lung fibrosis and inflammation, the lung hydroxyproline content, inflammation and fibrosis score were measured. Western blotting, zymography and reverse zymography were performed at 3, 7, 14, 28 days after bleomycin instillation in order to evaluate the MMP-2,-9, and TIMP-1,-2 expression level. ELISA was performed to determine the IL-4 and IL-13 level in a lung homogenate. Results : 1. 7 days after bleomycin instillation, inflammatory changes were more severe in the bleomycin+$IFN-{\gamma}$ group than the bleomycin group (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$2.08{\pm}0.15:2.74{\pm}0.29$, P<0.05), but 28 days after bleomycin instillation, lung fibrosis was significantly reduced as a result of the $IFN-{\gamma}$ treatment (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$3.94{\pm}0.43:2.64{\pm}0.13$, P<0.05). 2. 28 days after bleomycin instillation, the lung hydroxyproline content was significantly reduced as a result of $IFN-{\gamma}$ treatment (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$294.04{\pm}31.73{\mu}g/g:194.92{\pm}15.51{\mu}g/g$, P<0.05). 3. Western blotting showed that the MMP-2 level was increased as a result of the bleomycin instillation and highest in the 14 days after bleomycin instillation. 4. In zymography, the active forms of MMP-2 were significantly increased as a result of the $IFN-{\gamma}$ treatment 3 days after the bleomycin instillation, bleomycin+$IFN-{\gamma}$ group (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$209.63{\pm}7.60%:407.66{\pm}85.34%$, P<0.05), but 14 days after the bleomycin instillation, the active forms of MMP-2 were significantly reduced as a result of the $IFN-{\gamma}$ treatment (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$159.36{\pm}20.93%:97.23{\pm}12.50%$, P<0.05). 5. The IL-4 levels were lower in the bleomycin and bleomycin+$IFN-{\gamma}$ groups but this was not significant, and the IL-13 levels showed no difference between the experiment groups. Conclusion : The author found that lung inflammation was increased in the early period but the pulmonary fibrosis was inhibited in the late stage as a result of $IFN-{\gamma}$. The inhibition of pulmonary fibrosis by $IFN-{\gamma}$ appeared to be associated with the inhibition of MMP-2 activation by $IFN-{\gamma}$. Further studies on the mechanism of the regulation of MMP-2 activation and the effects of MMP-2 activation on pulmonary fibrosis is warranted in the future.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.6
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• pp.909-919
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• 2010

The purpose of this study was to investigate the demand and use of fresh-cut produce in school foodservice and restaurant industries. The subjects of this survey study were 200 school nutritionists and 70 cooks or managers in the restaurant industry nationwide. The data were collected by means of self-administered or e-mail questionnaires. Data analysis was completed using the SPSS window (ver. 12.0) program including frequency, $\chi^2$-test and t-test. Survey questions assessed the general characteristic of respondents, and the supply, use, and demand of fresh-cut produce in school foodservice and restaurant industries. Over 74% of the subjects have used fresh-cut produce. Most of the school foodservice (84.0%) kept fresh-cut produce for one day, while restaurant industry (28.3%) kept them up to three days. The nutritionists of school foodservice and managers of restaurant industry considered origin and date of production as the most important factor, respectively, when fresh-cut produce were being used. Fresh-cut root vegetable, such as potato and carrot was used mostly. The main reason not to use the fresh cut produce was due to the distrust of the fresh-cut produce safety in school foodservice and cost in restaurant industry. The main problem in fresh-cut produce use was the need of rewashing (29.9%) in school foodservice and irregular size (39.0%) in restaurant industry. These results indicate that the quality standard and size specification must be prepared with production guideline of safe fresh-cut produce.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.10
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• pp.1346-1355
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• 2012

This study was carried out to investigate the characteristics of black jujube and Zizyphus jujube extracts during lactic acid fermentation. Both extracts were fermented using Lactobacillus fermentum YL-3. As a result, viable cell number rapidly increased until 24 hours, after which it gradually decreased. Before lactic acid fermentation, the $IC_{50}$ of black jujube, which was 0.014 mg/mL, was lower than that of Zizyphus jujube. Further, black jujube showed stronger antioxidant activity (374.21 mg AA eq/g) than Zizyphus jujube. Contents of total polyphenolics in both extracts were 15.46 mg/g and 13.61 mg/g, respectively, whereas contents of total flavonoids were 374.21 ${\mu}g/g$ and 64.25 ${\mu}g/g$. After lactic acid fermentation, there was no significant increase in DPPH or ABTS free radical scavenging activity. Total polyphenolic content of Zizyphus jujube decreased to 12.39 mg/g upon fermentation, whereas flavonoid content significantly increased to 291.58 ${\mu}g/g$. Further, polyphenolic and flavonoid contents of black jujube increased from 15.46 mg/g to 17.46 mg/g and from 374.21 ${\mu}g/g$ to 1,135.29 ${\mu}g/g$, respectively. These results demonstrate that 9-Times Steamed and Dried increased functional components. Especially, lactic acid fermented black jujube showed remarkably high antioxidant activity. These results confirm the potential use of lactic acid fermented black jujube as a valuable resource for the development of functional foods.

• Tuberculosis and Respiratory Diseases
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• v.49 no.4
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• pp.486-494
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• 2000

Background : The dominant innervation of airway smooth muscle is parasympathetic fibers which are carried in the vagus nerve. Activation of these cholinergic nerves releases acetylcholine which binds to $M_3$ muscarinic receptors on the smooth muscle causing bronchocontraction. Acetylcholine also feeds back onto neuronal $M_2$ muscarinic receptors located on the postganglionic cholinergic nerves. Stimulation of these receptors further inhibits acetylcholine release, so these $M_2$, muscarinic receptors act as autoreceptors. Loss of function of these $M_2$ receptors, as it occurs in animal models of hyperresponsiveness, leads to an increase in vagally mediated hyperresponsiveness. However, there are limited data pertaining to whether there are dysfunctions of these receptors in patients with asthma. The aim of this study is to determine whether there are dysfunction of $M_2$ muscarinic receptors in asthmatic patients and difference of function of these receptors according to severity of asthma. Method : We studied twenty-seven patients with asthma who were registered at Pulmonology Division of Korea University Hospital. They all met asthma criteria of ATS. Of these patients, eleven patients were categorized as having mild asthma, eight patients moderate asthma and eight patients severe asthma according to severity by NAEPP Expert Panel Report 2(1997). All subjects were free of recent upper respiratory tract infection within 2 weeks and showed positive methacholine challenge test ($PC_{20}$<16mg/ml). Methacholine provocation tests were performed twice on separate days allowing for an interval of one week. In the second test, pretreatment with the $M_2$ muscarinic receptor agonist pilocarpine($180{\mu}g$) through inhalation was performed be fore the routine procedures. Results : Eleven subjects with mild asthma and eight subjects with moderate asthma showed significant increase of $PC_{20}$ from 5.30$\pm$5.23mg/ml(mean$\pm$SD) to 20.82$\pm$22.56mg/ml(p=0.004) and from 2.79$\pm$1.51mg/ml to 4.67$\pm$3.53mg/ml(p=0.012) after pilocarpine inhalation, respectively. However, in the eight subjects with severe asthma significant increase of $PC_{20}$ from l.76$\pm$1.50mg/ml to 3.18$\pm$4.03mg/ml(p=0.161) after pilocarpine inhalation was not found. Conclusion : In subjects with mild and moderate asthma, function of $M_2$ muscarinic receptors was normal, but there was a dysfunction of these receptors in subjects with severe asthma. ηlese results suggest that function of $M_2$ muscarinic receptors is different according to severity of asthma.

• Tuberculosis and Respiratory Diseases
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• v.45 no.4
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• pp.736-745
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• 1998

Background: Respiratory muscle interaction is further profoundly affected by a number of pathologic conditions. Hyperinflation may be particularly severe in chronic obstructive pulmonary disease(COPD) patients, in whom the functional residual capacity(FRC) often exceeds predicted total lung capacity(TLC). Hyperinflation reduces the diaphragmatic effectiveness as a pressure generator and reduces diaphragmatic contribution to chest wall motion. Ultrasonography has recently been shown to be a sensitive and reproducible method of assessing diaphragmatic excursion. This study was performed to evaluate how differences of diaphragmatic excursion measured by ultrasonography associate with normal subjects and COPD patients. Methods: We measured diaphragmatic excursions with ultrasonography on 28 healthy subjects(l6 medical students, 12 age-matched control) and 17 COPD patients. Ultrasonographic measurements were performed during tidal breathing and maximal respiratory efforts approximating vital capacity breathing using Aloka KEC-620 with 3.5 MHz transducer. Measurements were taken in the supine posture. The ultrasonographic probe was positioned transversely in the midclavicular line below the right subcostal margin. After detecting the right hemidiaphragm in the B-mode the ultrasound beam was then positioned so that it was approximately parallel to the movement of middle or posterior third of right diaphragm. Recordings in the M-mode at this position were made throughout the test. Measurements of diaphragmatic excursion on M-mode tracing were calculated by the average gap in 3 times-respiration cycle. Pulmonary function test(SensorMedics 2800), maximal inspiratory(PImax) and expiratory mouth pressure(PEmax, Vitalopower KH-101, Chest) were measured in the seated posture. Results: During the tidal breathing, diaphragmatic excursions were recorded $1.5{\pm}0.5cm$, $1.7{\pm}0.5cm$ and $1.5{\pm}0.6cm$ in medical students, age-matched control group and COPD patients, respectively. Diaphragm excursions during maximal respiratory efforts were significantly decreased in COPD patients ($3.7{\pm}1.3cm$) when compared with medical students, age-matched control group($6.7{\pm}1.3cm$, $5.8{\pm}1.2cm$, p< 0.05}. During maximal respiratory efforts in control subjects, diaphragm excursions were correlated with $FEV_1$, FEVl/FVC, PEF, PIF, and height. In COPD patients, diaphragm excursions during maximal respiratory efforts were correlated with PEmax(maximal expiratory pressure), age, and %FVC. In multiple regression analysis, the combination of PEmax and age was an independent marker of diaphragm excursions during maximal respiratory efforts with COPD patients. Conclusion: COPD subjects had smaller diaphragmatic excursions during maximal respiratory efforts than control subjects. During maximal respiratory efforts in COPD patients, diaphragm excursions were well correlated with PEmax. These results suggest that diaphragm excursions during maximal respiratory efforts with COPD patients may be valuable at predicting the pulmonary function.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of the Daesoon Academy of Sciences
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• v.33
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• pp.31-62
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• 2019

The purpose of this article is to study on the Daesoon Cosmology of the Correlative Relation between Mugeuk and Taegeuk. Daesoon cosmology is a cosmology based on the juxtaposition between the Gucheon Sangje and the world. In this article, I would like to say that this theory in Daesoon Thought was developed in three stages: the phase of the Mugeuk Transcendence of Gucheon Sangje, the stage of the Taegeuk Immanence, and the phase of the Grand Opening of the Later World between Mugeuk and Taegeuk as a correlative gentle reign. First of all, the phase of the Mugeuk Transcendence of Gucheon Sangje has been revealed as a yin-yang relationship. The stage of the Taegeuk Immanence represents the togetherness of harmony and co-prosperity between yin and yang, and the phase of the Grand Opening of the Later World between Mukeuk and Taegeuk refers to the unshakable accomplishment of its character and energy. It will be said that this is due to the practical mechanism in the correct balance of yin-yang making a four stage cycle of birth, growth, harvest, and storage. In addition, the Daesoon stage of the settlement of yin and yang is revealed as a change in the growth of all things and the formation of the inner circle. The mental growth reveals the characteristics of everything in the world, each trying to shine at the height of their own respective life as they grow up energetically. The dominant culture of cerebral communion renders a soft and elegant mood and combines yin and yang to elevate the heavenly and earthly period through transcendental change into sympathetic understanding. The stage of the Grand Opening of the Later World between Mugeuk and Taegeuk is one of the earliest days of the lunar month and also the inner circle of Taegeuk. It is in line with Ken Wilbur's integrated model as a step to the true degrees to develop into a world with brightened degrees. It is a beautiful and peaceful scene where celestial maidens play music, the firewood burns, and the scholars command thunder and lightning playfully. Human beings achieve a state of happiness as a free beings who lives as gods upon the earth. This is the world of theGrand Opening of the Later World between Mugeuk and Taegeuk. Daesoon Thought was succeeded by Dojeon in 1958, when Dojeon emerged as the successor in the lineage of religious orthodoxy and was assigned the task of handling Dao in its entirety. In addition, Daesoon is a circle and represents freedom and commonly shared happiness among the populous. Cosmology in the Daesoon Thought will enable us to understand deep dimensions and the identity of members as individuals within an inner circle of correlation between transcendence and immanence. This present study tries to analyze the public effects philologically and also the mutual correlation by utilizing the truthfulness of literature and rational interpretation. The outlook for the future in Daesoon Thought also leads to the one-way communication of Daesoon as a circle.