• Journal of the Daesoon Academy of Sciences
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• v.47
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• pp.241-279
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• 2023

The Scripture of Myriad Laws was first published in 1986 and then reprinted in 1994 and 1995. It gained widespread recognition as a mysterious text or a Buddhist document containing enigmatic content that deemed difficult to comprehend. Through the analysis of the content of The Scripture of Myriad Laws, it was revealed that the book was published by the Dragon Flower Order, a Jeungsanist religion founded by Seo Baek-Il (徐白一). Therefore, the various texts included in The Scripture of Myriad Laws can be classified as 'Songs of Jeungsanism' (Jeungsan-gyo Gasa 甑山敎歌辭). The contents included in The Scripture of Myriad Laws often mention terms unique to the Jeungsanist orders, such as 'the Reordering Works of Heaven and Earth' (天地公事), 'presiding over cures' (醫統), 'Degree Number' (度數), 'the West God' (西神), 'the nobility of heaven' (天尊), 'the nobility of earth' (地尊), 'the nobility of humanity' (人尊), 'ruling the world for 50 years' (治天下五十年), and 'the era of Resolving Grievances (解冤時代).' It also discusses the birthplace and birth date of Kang Jeungsan, his family name, and the duration of his existence. The contents directly quote the words spoken by Jeungsan and incorporate them into songs. They also mention unique Jeungsan terms such as 'Five Immortals Playing Baduk' (五仙圍碁), 'open-weight wresting match,' 'birth, growth, harvest, and storage' (生長斂藏), 'the god who listens to words' (言聽神), 'pillar of foundation' (基礎棟樑),' 'Ocean Seal' (海印), and 'the higher gods' (上計神). It is also notable that some verses of Chinese poetry that Jeungsan taught his disciples are directly quoted in this scripture. Furthermore, the scripture shows traces of Buddhist salvational beliefs; particularly those that emphasize faith in Maitreya Bodhisattva (彌勒信仰). However, The Scripture of Myriad Laws differs from traditional Buddhist beliefs in that it anticipates and emphasizes the birth of a specific individual endowed with the power and abilities of Maitreya Buddha. While emphasizing Maitreya Buddha (彌勒世尊) and the Dragon Flower Gathering (龍華會上), the scripture also specifically mentions Geumsan-sa Temple (金山寺) located on Mount Moak (母岳山) in North Jeolla Province, and these details are sung about in a special manner. This positive portrayal serves to affirm the belief of followers that Jeungsan, centered around Geumsan-sa Temple, was an incarnation of Maitreya Buddha. Moreover, The Scripture of Myriad Laws subtly asserts that Seo Baek-il, the leader of the Dragon Flower Order, who is mentioned in the scripture, is the absolute savior who has come to this world in place of Jeungsan. In support of this teaching, his birth date, birthplace, years of imprisonment, release date, and honorific name (號) are all recorded in precise detail.

• Bangmulgwan gwa yeongu (The National Museum of Korea Journal)
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• v.1
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• pp.278-305
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• 2024

Gelatin dry plate photographs dating to the Japanese colonial era and the official documents from the Japanese Government-General of Korea Museum in the collection of the National Museum of Korea are significant materials documenting cultural heritage in North Korea before it was severely damaged in 1950 during the Korean War. There has been an increase in recent years in studies of Buddhist sculptures in North Korea based on these photographs and documents. This paper presents some new comments on the Buddhist sculptures at Seongbulsa Temple in Hwangju, one of the most famous temples in Hwanghae-do Province, based on the related existing research outcomes. This paper aims to facilitate a more comprehensive understanding of the Buddhist sculptures at Seongbulsa Temple by chronicling its history based on historical records, examining its current status, and exploring in detail the production dates and backgrounds of the Buddhist sculptures featured on gelatin dry plates. Prior to Korea's liberation from Japan in 1945, Seongbulsa housed at least seven sculptural items: two Bodhisattva statues, four Buddha statues, and a triad. Two items are from the early Goryeo period, one is from the late Goryeo period, three are from the early Joseon period, and one is from the late Joseon period. Among them, two surviving items are noteworthy. One is the early Goryeo-era Stone Seated Bhaishajyaguru Buddha photographed in Eungjinjeon Hall at Seongbulsa Temple. A close examination of a schematic drawing of the sculpture's pedestal made at the time it was photographed reveals that its material accords with the materials used for the headless Stone Seated Bhaishajyaguru Buddha and pedestal currently found in the old Sangwonam Hermitage site in the Inner Geumgang Valley of Jeongbangsan Mountain. This accordance could mean that the statue is a new significant example of early Goryeo Buddhist sculpture in North Korea. The other notable sculpture is the Gilt-bronze Seated Amitabha Buddha Triad created in 1454 (the second year of the reign of King Danjong) and discovered in Geungnakjeon Hall at Seongbulsa. This statue is currently in the collection of the Sariwon History Museum in Hwanghae-do Province. It is an important example of a dated small gilt-bronze Buddhist statue from the early Joseon period found in North Korea. This paper is a case study of Buddhist sculptures in North Korea, focusing on Seongbulsa Temple. Further utilization of the National Museum of Korea's gelatin dry plates will contribute to developing the study of the history of Korean Buddhist sculpture.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.53-68
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• 2024

Generative AI is receiving a lot of attention around the world, and ways to effectively utilize it in the business environment are being explored. In particular, since the public release of the ChatGPT service, which applies the GPT-3.5 model, a large language model developed by OpenAI, it has attracted more attention and has had a significant impact on the entire industry. This study focuses on the emergence of Generative AI, especially ChatGPT, which applies OpenAI's GPT-3.5 model, to investigate its impact on the startup industry and compare the changes that occurred before and after its emergence. This study aims to shed light on the actual application and impact of generative AI in the business environment by examining in detail how generative AI is being used in the startup industry and analyzing the impact of ChatGPT's emergence on the industry. To this end, we collected company information of generative AI-related startups that appeared before and after the ChatGPT announcement and analyzed changes in industry, business content, and investment information. Through keyword analysis, topic modeling, and network analysis, we identified trends in the startup industry and how the introduction of generative AI has revolutionized the startup industry. As a result of the study, we found that the number of startups related to Generative AI has increased since the emergence of ChatGPT, and in particular, the total and average amount of funding for Generative AI-related startups has increased significantly. We also found that various industries are attempting to apply Generative AI technology, and the development of services and products such as enterprise applications and SaaS using Generative AI has been actively promoted, influencing the emergence of new business models. The findings of this study confirm the impact of Generative AI on the startup industry and contribute to our understanding of how the emergence of this innovative new technology can change the business ecosystem.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• The Korean Journal of Rehabilitation Nursing
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• v.2 no.2
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• pp.257-270
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• 1999

The purpose of this study is to investigate factors related to the intent of using home nursing of chronic disease patients who got out of a university hospital. For the purpose, the study selected 153 patients who were hospitalized and left K university hospital with diagnoses of cancer, hypertension, diabetes and cerebral vascular accident and ordered to be discharged and performed interviews with them and surveys on their medical records to obtain the following results. For this study a direct-interview survey and medical record review was conducted from June 28 to Aug. 30, 1998. The frequency and mean values were computed to find the characteristics of the study subjects, and $X^2$-test, t-test, factor analysis and multiple logistic regession analysis were applied for the analysis of the data. The following results were obtained. 1) When characteristics of the subjects were examined, men and women occupied for 58.8% and 41.2%, respectively. The subjects were 41.3 years old in aver age and had the monthly aver age earning of 0.99 million won or below, which was the most out of the total subjects at 34.6%. Among the total, 87.6% resided in cities and 12.4 in counties. The most left the hospital with diagnosis of cancer at 51.6%, followed by hyper tension at 24.2%, diabetes at 13.7% and cerebral vascular accident at 7.2%. 2) 93.5% of the selected patients had the intent of using home nursing and 6.5%, didn't. Among those patients having the intent, 85.6% had the intent of paying for home nursing and 14.4%, didn't. The subjects expected that the nursing would be paid 9,143 won in aver age and 47.7% of them preferred national authorities as the main servers. 86.3% of the subjects thought that home nursing business had the main advantage of making it possible to learn nursing methods at home and thereby contributing to improving the ability of patients and their facilities to solve health problems. 3) Relations between the intent of use and characteristics of the subjects such as demography-related social, home environment, disease and physical function characteristics did not show statistically significant differences among one another. Compared to those who had no intent of using home nursing, the group having the intent had more cases of male patients, the age of 39 or below, residence in cities, 5 family member s or more, no existence of home nursing servers, leaving the hospital from a non-hospitalized building, disease development for five months or below, hospitalization for ten days or more, non-hospitalization with in the recent one month, two times or over of hospitalization, leaving the hospital with no demand of special treatment, operation underwent, poor results of treatment, leaving the hospital with demand of rehabilitation services, physical disablement and high evaluation point of daily life. 4) Among those patients having the intent of using home nursing, 47.6% demanded technical nursing and 55.9%, supportive nursing. As technical nursing,' inject into a blood vessel ' and 'treat pustule and teach basic prevention methods occupied for 57.4%, respectively, topping the list. Among demands of supportive nursing, 'observe patients 'status and refer them to hospitals or community resources as available, if necessary' was the most with percent age point of 59.5. Regarding the intent of paying for home nursing, 39.2% of those patients wishing to use the nursing responded paying for technical services and 20.2, supportive services. In detail, 70.0% wanted to pay for a service stated as 'inject into a blood vessel', highest among the former services and 30.7%, a service referred to as 'teaching exercises needed to make the body of patients move', highest among the latter. When this was analyzed in terms of a relation between the need(the need for home nursing) and the demand(the intent of paying for home nursing), The rate of the need to the demand was found two or three times higher in technical nursing(0.82) than in supportive nursing(0.35). In aspects of tech ical nursing, muscle injection(1.26, the 1st rank) was highest in the rate while among aspects of supportive nursing, a service referred to as 'teach exercises needed for making patients move their bodies normally'(0.58, the 1st rank). 5) factors I(satisfaction with hospital services), II(recognition of disease state), III(economy) and IV(period of disease) occupied for 34.4, 13.8, 11.9 and 9.2 percents, respectively among factors related to the intent by the subjects of using home nursing, totaled 59.3%. In conclusion, most of chronic disease patients have the intent of using hospital-based home nursing and satisfaction with hospital services is a factor affecting the intent most. Thus a post-management system is needed to continue providing health management to those patients after they leave the hospital. Further, supportive services should be provided in order that those who are satisfied with hospital services return to their community and live their in dependent lives. Based on these results, the researcher would make the following recommendation. 1) Because home nursing becomes more and more needed due to a sharp increase in chronic disease patients and elderly people, related rules and regulations should be made and implemented. 2) Hospital nurses specializing in home nursing should be cultivated.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.17-26
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• 2012

Purpose: This study mainly focuses on the patients treated with chemically stable radiopharmaceutical product $^{99m}Tc$-HMPAO (d,l-hexamethylpropylene amine oxime) which yielded reduced image quality due to a decreased brain extraction rate. $^{99m}Tc$-HMPAO will be examined further to determine whether this product may be accounted as a factor for this cause. Material and Methods: From January 2010 until December 2010, out of 272 patients who were all subjected to $^{99m}Tc$-HMPAO brain blood flow SPECT scans resulting from Cerebral Infarction; 23 patients(ages $55.3{\pm}9$, 21 males, 3 females) with decreased tissue extraction rate were examined in detail. The radiopharmaceutical product $^{99m}Tc$-HMPAO was used on patients with normal brain tissue exchange rate as well as those with reduced rate in order to prove its' chemical stability. The patients' age, sex, blood pressure, existence of diabetes, drug use, current health status, known side effects from CT/MRI, examination of the patients' past SPECT before/after images were accounted to determine the factors and correlations affecting the rate of blood tissue extractions. Result: After multiple linear regression analysis, there were no unusual correlations between the 6 factors excluding sex, and before/after examination images. Male subjects showed reduced brain tissue extraction rate than the females ($p$ > 0.05) 91.3% male, 8.7% female. Wilcoxon Matched-Pairs Signed-Ranks Test was used on the before/after images which yielded a value of 0.06, which did not indicate a significant amount of difference on the 2 tests ($p$ > 0.05). As a result, the before/after images indicated similar brain tissue extraction rates, and there were variations depending on the individual patient. Conclusion: The effects of the chemically stable radiopharmaceutical product $^{99m}Tc$-HMPAO depended on the patient's personal characteristics and status, therefore was considered to be a factor in reducing brain tissue extraction rate. The related articles of $^{99m}Tc$-HMPAO cerebral blood flow SPECT speculates a cerebrovascular disease and factors resulting from portal veins, and it was not possible to pin point the exact cause of decreasing brain tissue extraction rate. However, the $^{99m}Tc$-HMPAO cerebral blood flow SPECT scan proved to be extremely useful in tracking and inspecting brain diseases, as well as offering accurate results from patients suffering from reduced brain tissue extraction rates.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.9-16
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• 2010

Purpose: With increasing medical use of radiation and radioactive isotopes, there is a need to better manage the risk of radiation exposure. This study aims to grasp and analyze the individual radiation exposure situations of radiation-related workers in a medical facility by specific job, in order to instill awareness of radiation danger and to assist in safety and radiation exposure management for such workers. Materials and Methods: 1 January 2007 to 31 December 2009 to work in medical institutions are classified as radiation workers Nuclear personal radiation dosimeter regularly, continuously administered survey of 40 workers in three years of occupation to target, Imaging Unit beautifully, age, dose sector, job function-related tasks to identify the average annual dose for a deep dose, respectively, were analyzed. The frequency analysis and ANOVA analysis was performed. Results: Imaging Unit beautifully three years the annual dose PET and PET/CT in the work room 11.06~12.62 mSv dose showed the highest, gamma camera injection room 11.72 mSv with a higher average annual dose of occupation by the clinical technicians 8.92 mSv the highest, radiological 7.50 mSv, a nurse 2.61 mSv, the researchers 0.69 mSv, received 0.48 mSv, 0.35 mSv doctors orderly, and detail work employed the average annual dose of the PET and PET/CT work is 12.09 mSv showed the highest radiation dose, gamma camera injection work the 11.72 mSv, gamma camera imaging work 4.92 mSv, treatment and safety management and 2.98 mSv, a nurse working 2.96 mSv, management of 1.72 mSv, work image analysis 0.92 mSv, reading task 0.54 mSv, with receiving 0.51 mSv, 0.29 mSv research work, respectively. Dose sector average annual dose of the study subjects, 15 people (37.5%) than the 1 mSv dose distribution and 5 people (12.5%) and 1.01~5.0 mSv with the dose distribution was less than, 5.01~10.0 mSv in the 14 people (35.0%), 10.01~20.0 mSv in the 6 people (15.0%) of the distribution were analyzed. The average annual dose according to age in occupations that radiological workers 25~34 years old have the highest average of 8.69 mSv dose showed the average annual dose of tenure of 5~9 years in jobs radiation workers in the 9.5 mSv The average was the highest dose. Conclusion: These results suggest that medical radiation workers working in Nuclear Medicine radiation safety management of the majority of the current were carried out in the effectiveness, depending on job characteristics has been found that many differences. However, this requires efforts to minimize radiation exposure, and systematic training for them and for reasonable radiation exposure management system is needed.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Korean Journal of Poultry Science
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• v.33 no.3
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• pp.171-179
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• 2006

Changes in the chicken testis from hatching to adulthood were studied in Korean native chickens of 1, 2, 4, 6, 8, 10, 12, 14, 16, 18, 21, 24, 28, 32, 44, 52 and 64 weeks (n=13 chickens per group) of age. The present study was to investigate in more detail the post-hatching development of testis in Korean native chickens. Testes of chickens were fixed by whole body perfusion using a fixative containing 2.5% glutaraldehyde in cacodylate buffer, processed and embedded in Epon-araldite. Using $1{\mu}m$ sections stained with methylene blue-azure II, qualitative and quantitative(stereological) morphological studies were performed. Sperm production was measured by routine technique. The average volume of a testis of 1 week old Korean native chickens was determined as 0.015 g and the parameter increased linearly from 1 week to 21 weeks days (28.9 g), and did not change from 21 weeks to 64 weeks. The volume density of the seminiferous tubules increased with age from 32.6% at week 1 to 92.89% at week 64. The volume density of the interstitium represents 67.4% of the testicular parenchyma at week 1. This proportion progressively diminished during development to reach a value of 7.11% at week 64. Total sperm production per testis increased significantly from 18 weeks to 28 weeks and remained unchanged. Sperm production per 1 g testis increased significantly from 18 weeks to 28 weeks, did not change significantly from 28 weeks to 52 weeks, and declined significantly at 64 weeks of age. The average diameter of the seminiferous tubules gradually increased with age from 1 week $(42.4{\mu}m)$ to 21 weeks $(412.8{\mu}m)$. The length of the seminiferous tubules was 0.34 m at 1 week, increased significantly in subsequent age groups and reached 72.2 m by weeks 64. The stage of germ cell development in seminiferous tubules was classified as 1) spermatogonia $(1\sim8\;weeks)$, 2) spermatogonia and spermatocytes $(10\sim12\;weeks)$, 3) spermatogonia, spermatocytes and round spermatids $(14\sim16\;weeks)$, and 4) speramatogonia, spermatocytes, spermatids and spermatozoa $(18\sim64\;weeks)$. These results clarified the pattern of changes in the testicular development in Korean native chickens from hatching to adulthood as 1) neonatal-prepubertal $(1\sim12\;weeks)$, 2) puberty$(14\sim18\;weeks)$, and adult$(21\sim64\;weeks)$.