• Tuberculosis and Respiratory Diseases
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• v.43 no.6
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• pp.954-964
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• 1996

Background : The type of the infiltrating cells in al veolitis may be determined by the chemokines in the lesion. MIP-1 ${\alpha}$, a C-C type chemokine, stimulates proliferation and cytokine secretion from macrophages and induces early neutrophilic and later monocytic inflammation in vi vo. IL-8, a C-X-C type chemokine is known to attract neutrophils and T-lymphocytes. This study is performed to find out the relative role of two different chemokines in diffuse interstitial lung disease. Subject and Method : We measured the secretion of MIP- 1 ${\alpha}$ and IL-8 from alveolar macrophages(AM), and their level in BAL fluid of 26 patients with DILD (10 IPF, 4 collagen disease, 10 sarcoidosis, and 2 hypersensitivity pneumonitis) and 7 normal control. Result: IL-8 secretion was significantly increased in patients with DILD ($8.15{\pm}4.58$ ng/ml) than in normal ($1.10{\pm}0.93$ ng/ml, p=0.0003). Significant correlation was found between IL-8 secretion and total cell number in BAL fluid (r=0.484, p=0.0068), %(r=0.592, p=0.0004) and No. (r=0.516, p=0.0042) of lymphocyte, and % of AM (r=-0.505, 0.0032). MIP- 1 ${\alpha}$ secretion was also increased in DILD ($2.41{\pm}1.45$ ng/ml) compared to control ($0.63{\pm}0.30$ ng/ml, p=0.0031), and showed a tendency of correlation with total cell number (r=0.368, p=0.0456) and No. of alveolar macrophages (r=0.356, p=0.0579) in BAL fluid. The concentration of IL-8 in BAL fluid was significantly increased in the patients with DILD ($40.4{\pm}34.5$ pg/ml) compared to control ($3.90{\pm}2.47$ pg/ml, p=0.0094) and it showed a significant correlation with the total cell number (r=0.484, p=0.0068), %(r=-0.505, p=0.0032) of AM, and % (r=0.592, p=0.0004) and No. (r=0.516, p=0.0042) of lymphocyte in BAL fluid. But there was a no significant difference in MIP- 1 ${\alpha}$ concentration in BAL fluid between normal control group and the patients with DILD. Conclusion : From the above results, we concluded that AM of DILD releases increased amount of both IL-8 and MIP- 1 ${\alpha}$ but IL-8 has better correlation with the type of alveolitis.

• Journal of agricultural medicine and community health
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• v.25 no.1
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• pp.29-49
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• 2000

Objectives: To estimate the prevalence rate of hypertension, the changes of health behavior, and compliance for the drug treatment after diagnosed as hypertension. Methods: 7,030 persons who live in Cheonan City of Chungnam Province were selected by the cluster sampling method, and 5,372 persons were surveyed by questionnaire and health examination. This data is analyzed by Chi-square test on each variable. Results: 49.8%- of men and 38.8%- of women had been diagnosed as hypertension, and the prevalence rate of hypertension was significantly increased with aging in both gender. The prevalence rate tended to decrease in highly educated women group. Unemployed persons or obese persons showed relatively higher prevalence rate. The prevalence rate of hypertension increased in groups with higher total cholesterol levels over 240 mg/dl, and groups with glucose level over 200 mg/dl. 53.1%- of male patients and 66.6%- of female patients showed compliance for antihypertensive treatment. Compliance for treatment was higher in aged group or lower educated group in both gender. Among men, proportion of compliant subjects was higher in unemployed group(49.3%-), and lower in labor or primary industry than the others but among women, there was not any significant difference. And men with compliance for treatment had higher monthly income than the others, but women did not show any. Conclusion : This population had a high prevalence rate of hypertension which may lead to cardiovascular disease. Therefore health education programs and distribution of information must be emphasized in order to increase compliance to treatment and encourage the change of health behavior to promote health.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.51-69
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• 2020

Now, it is a stylized fact that a small number of technology firms such as Apple, Alphabet, Microsoft, Amazon, Facebook and a few others have become larger and dominant players in an industry. Coupled with the rise of these leading firms, we have also observed that a large number of young firms have become an acquisition target in their early IPO stages. This indeed results in a sharp decline in the number of new entries in public exchanges although a series of policy reforms have been promulgated to foster competition through an increase in new entries. Given the observed industry trend in recent decades, a number of studies have reported increased concentration in most developed countries. However, it is less understood as to what caused an increase in industry concentration. In this paper, we uncover the mechanisms by which industries have become concentrated over the last decades by tracing the changes in industry concentration associated with a firm's status change in its early IPO stages. To this end, we put emphasis on the case in which firms are acquired shortly after they went public. Especially, with the transition to digital-based economies, it is imperative for incumbent firms to adapt and keep pace with new ICT and related intelligent systems. For instance, after the acquisition of a young firm equipped with AI-based solutions, an incumbent firm may better respond to a change in customer taste and preference by integrating acquired AI solutions and analytics skills into multiple business processes. Accordingly, it is not unusual for young ICT firms become an attractive acquisition target. To examine the role of M&As involved with young firms in reshaping the level of industry concentration, we identify a firm's status in early post-IPO stages over the sample periods spanning from 1990 to 2016 as follows: i) being delisted, ii) being standalone firms and iii) being acquired. According to our analysis, firms that have conducted IPO since 2000s have been acquired by incumbent firms at a relatively quicker time than those that did IPO in previous generations. We also show a greater acquisition rate for IPO firms in the ICT sector compared with their counterparts in other sectors. Our results based on multinomial logit models suggest that a large number of IPO firms have been acquired in their early post-IPO lives despite their financial soundness. Specifically, we show that IPO firms are likely to be acquired rather than be delisted due to financial distress in early IPO stages when they are more profitable, more mature or less leveraged. For those IPO firms with venture capital backup have also become an acquisition target more frequently. As a larger number of firms are acquired shortly after their IPO, our results show increased concentration. While providing limited evidence on the impact of large incumbent firms in explaining the change in industry concentration, our results show that the large firms' effect on industry concentration are pronounced in the ICT sector. This result possibly captures the current trend that a few tech giants such as Alphabet, Apple and Facebook continue to increase their market share. In addition, compared with the acquisitions of non-ICT firms, the concentration impact of IPO firms in early stages becomes larger when ICT firms are acquired as a target. Our study makes new contributions. To our best knowledge, this is one of a few studies that link a firm's post-IPO status to associated changes in industry concentration. Although some studies have addressed concentration issues, their primary focus was on market power or proprietary software. Contrast to earlier studies, we are able to uncover the mechanism by which industries have become concentrated by placing emphasis on M&As involving young IPO firms. Interestingly, the concentration impact of IPO firm acquisitions are magnified when a large incumbent firms are involved as an acquirer. This leads us to infer the underlying reasons as to why industries have become more concentrated with a favor of large firms in recent decades. Overall, our study sheds new light on the literature by providing a plausible explanation as to why industries have become concentrated.

• Childhood Kidney Diseases
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• v.10 no.1
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• pp.8-17
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• 2006

Purpose : We describe the changes of rat glomerular epithelial cells when exposed to high levels of glucose and advanced glycosylation endproducts(AGE) in the in vitro diabetic condition. We expect morphological alteration of glomerular epithelial cells and permeability changes experimentally and we may correlate the results with a mechanism of proteinuria in DM. Methods : We made 0.2 M glucose-6-phsphate solution mixed with PBS(pH 7.4) containing 50 mg/mL BSA and pretense inhibitor for preparation of AGE. As control, we used BSA. We manufactured and symbolized five culture dishes as follows; B5 - normal glucose(5 mM) + BSA, B30 - high glucose(30 mM) + BSA, A5 - normal glucose(5 mM) + AGE, A30 - high glucose(30 mM) + AGE, A/B 25 - normal glucose(5 mM) + 25 mM of mannitol(osmotic control). After the incubation period of both two days and seven days, we measured the amount of heparan sulfate proteoglycan(HSPG) in each dish by ELISA and compared them with the B5 dish at 2nd and 7th incubation days. We observed the morphological changes of epithelial cells in each culture dish using scanning electron microscopy(SEM). We tried the permeability assay of glomerular epithelial cells using cellulose semi-permeable membrane measuring the amount of filtered BSA through the apical chamber for 2 hours by sandwich ELISA. Results : On the 2nd incubation day, there was no significant difference in the amount of HSPG between the 5 culture dishes. But on the 7th incubation day, the amount of HSPG increased by 10% compared with the B5 dish on the 2nd day except the A30 dish(P<0.05). Compared with the B5 dish on the 7th day the amount of HSPG in A30 and B30 dish decreased to 77.8% and 95.3% of baseline, respectively(P>0.05). In the osmotic control group (A/B 25) no significant correlation was observed. On the SEM, we could see the separated intercellular junction and fused microvilli of glomerular epithelial cells in the culture dishes where AGE was added. The permeability of BSA increased by 19% only in the A30 dish on the 7th day compared with B5 dish on the 7th day in the permeability assay(P<0.05). Conclusion: We observed not only the role of a high level of glucose and AGE in decreasing the production of HSPG of glomerular epithelial cells in vitro, but also their additive effect. However, the role of AGE is greater than that of glucose. These results seems to correlate with the defects in charge selective barrier. Morphological changes of the disruption of intercellular junction and fused microvilli of glomerular epithelial cells seem to correlate with the defects in size-selective barrier. Therefore, we can explain the increased permeability of glomerular epithelial units in the in vitro diabetic condition.

• Journal of Distribution Research
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• v.15 no.3
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• pp.1-34
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• 2010

Introduction The purpose of this research is to develop overall model which involves the effect of ongoing support services by franchisor on franchisee's relationship quality(trust, satisfaction, and commitment) and business performance(financial and non-financial performance), and to investigate the relationships among trust, satisfaction, commitment, financial and non-financial performance. This study also suggests franchise business or franchise system should be based on long-term orientation between franchisor and franchisee rather than short-term orientation, or transactional relationship, and proposes the most effective way of providing on-going support services by franchisor with franchisee thru symbiotic relationship among franchisor and franchisee Research Model and Hypothesis The research model as Figure 1 shows the variables on-going support services which affect the relationship quality between franchisor and franchisee such as trust, satisfaction, and commitment, and also analyze the effects of relationship quality on business performance including financial and non-financial performance We established 12 hypotheses to test as follows; Relationship between on-going support services and trust H1: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's trust. Relationship between on-going support services and satisfaction H2: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's satisfaction. Relationship between on-going support services and commitment H3: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's commitment. Relationship among relationship quality: trust, satisfaction, and commitment H4: Franchisee's trust has positive effect on franchisee's satisfaction. H5: Franchisee's trust has positive effect on franchisee's commitment. H6: Franchisee's satisfaction has positive effect on franchisee's commitment. Relationship between relationship quality and business performance H7: Franchisee's trust has positive effect on franchisee's financial performance. H8: Franchisee's trust has positive effect on franchisee's non-financial performance. H9: Franchisee's satisfaction has positive effect on franchisee's financial performance. H10: Franchisee's satisfaction has positive effect on franchisee's non-financial performance. H11: Franchisee's commitment has positive effect on franchisee's financial performance. H12: Franchisee's commitment has positive effect on franchisee's non-financial performance. Method The on-going support services were defined as an organized system of continuous supporting services by franchisor for the purpose of satisfying the expectation of franchisee based on long-term orientation and classified into six constructs such as product category & price, logistics service, promotion, providing information & problem solving capability, supervisor's support, and education & training support. The six constructs were measured agreement using a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree)as follows. The product category & price was measured by four items: menu variety, price of food material provided by franchisor, and support for developing new menu. The logistics service was measured by six items: distribution system of franchisor, return policy for provided food materials, timeliness, inventory control level of franchisor, accuracy of order, and flexibility of emergency order. The promotion was measured by five items: differentiated promotion activities, brand image of franchisor, promotion effect such as customer increase, long-term plan of promotion, and micro-marketing concept in promotion. The providing information & problem solving capability was measured by information providing of new products, information of competitors, information of cost reduction, and efforts for solving problems in franchisee's operations. The supervisor's support was measured by supervisor operations, frequency of visiting franchisee, support by data analysis, processing the suggestions by franchisee, diagnosis and solutions for the franchisee's operations, and support for increasing sales in franchisee. Finally, the of education & training support was measured by recipe training by specialist, service training for store people, systemized training program, and tax & human resources support services. Analysis and results The data were analyzed using Amos. Figure 2 and Table 1 present the result of the structural equation model. Implications The results of this research are as follows: Firstly, the factors of product category, information providing and problem solving capacity influence only franchisee's satisfaction and commitment. Secondly, logistic services and supervising factors influence only trust and satisfaction. Thirdly, continuing education and training factors influence only franchisee's trust and commitment. Fourthly, sales promotion factor influences all the relationship quality representing trust, satisfaction, and commitment. Fifthly, regarding relationship among relationship quality, trust positively influences satisfaction, however, does not directly influence commitment, but satisfaction positively affects commitment. Therefore, satisfaction plays a mediating role between trust and commitment. Sixthly, trust positively influence only financial performance, and satisfaction and commitment influence positively both financial and non-financial performance.

• Korean Journal of Heritage: History & Science
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• v.45 no.3
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• pp.60-79
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• 2012

Environment of ancient Chinese Buddhist temple can be classified to three types such as regional society(鄕村), famous mountain(名山), and urban areas(都市). This made differences in environment where a temple existed and in turn, affected development of Buddhism. And this made another type in relationship between Buddhist temple and a society. This study explains influences which regional society gave on not only Buddhist temple and a monk but also existence and development of Buddhism. When temples are placed in different environmental position, that is, urban areas and regional society, among a social structure, they eventually should adapt to a different society externally and internally. As told in above, ancient Chinese Buddhist temple was located in regional society, famous mountain, and urban areas. Since Eastern Jin and Sixteen Kingdoms, as number of temple much increased, and temples and monks were concentrated on famous mountain, temples in famous mountains and urban areas had developed showing similar aspects each other. But because temples in regional society were influenced a little differently, this study focused on the point. There are four kinds of influences between temples and monks in regional areas. Monks in regional areas had a comparatively close relationship with a society because they came from same area or surrounding areas. Therefore,powers of regional areas restrict influences made by monk group in temple. Second, temples in regional areas shared their joys and sorrows depending on regional economy. Temples in regional areas became a public place for the society and often a market place. In fact, construction and existence of a temple originally became a driving force in regional economy. This is because construction of temple needs artisans and materials and some temples had visitors and included market economy like consumption of incense and candles, though the economic size was large or small. And when regional areas experienced natural disaster or man-made disaster or had poor harvest or economy was in depression, monks left temples and then, temples themselves could not exist. Third, the relationship between temples in regional areas and Buddhists was distinguished from the temples in urban areas and famous mountains. This is because temples in China were places where monks practiced and at the same time, places where general Buddhists worshipped. So there were always a number of Buddhists around the temples. Forth, Buddhism in resional areas was connected to regional Folk beliefs. As a result, Buddhism was spread across the nation, worship with local color often was changed to Buddhist belief or was tinged with Buddhism. While temples in regional areas maintained a close relationship with regional society.they were influenced by the region or gave influences. As a representative example, temples in regional areas showed model behaviors instead of roles of facilities related to various cultures with comparatively advanced level - for example, school, hospital etc. The temples highly affected funerary rites in regional areas. Chinese tombs were mainlymade in regional areas. After death,people living in urban areas were buried in hometown or at least, they were buried in suburbs not urban areas. Temples in regional areas generally participated in funerary rites. Above shows that though most of famous Buddhist temples were located in urban areas not in famous mountains,majority of temples were located in vast regional areas. Through mutual interaction between temples and regional society, the temples in the regional areas were related to Chinese people of over 90% and regional areas became the most important foundation for Buddhism in China. Mutual influences between temples in regional areas and the general public in regions were omnidirectional and spreaded to every aspects of social life in small or large degree. Thus Tombs in temple were widely spreaded across regional areas over time and space. This is enough to explain a close relationship between Buddhist temples and rural society in ancient China.

• Korean Journal of Heritage: History & Science
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• v.49 no.2
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• pp.4-19
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• 2016

Jangkei(狀啓) made to the Royal Court by Yi Sun-sin during the Japanese invasions of Korea is handed down under the names of Jangcho(狀草), Keicho(啓草), Keibon(啓本) and others depending on copying patterns of those times and later times as it was copied out by a third person. In particular, "YimjinJangcho(壬辰狀草)" which Yi drew up during his service as the director of the naval forces in Jeolla Jwasooyeong is known as the most popular Jangkei. "ChungMinKongKeicho" which has been re-located recently after loss is a national treasure level cultural property as valuable as "YimjinJangcho" and should be treated as a model of Yi Sun-sin's other Jangkeis by next generations. As of now, however it is not confirmed if it is a totally new book related to Yi Sun-sin or is supplementary to the lost Jangkei, this study decided to ascertain relevant information through a bibliographic discussion on the question. "Chungmin(忠愍)" was the title that was used after the death of Yi Sun-sin, and "ChungMinKongKeicho" was completed when Jangkei was copied in 1662. 12 books that would not be found in YimjinJangcho are included in the book and such books are also present in the Jangkei supplement which has been known lost so far. What should be especially focused on here is that the forms and contents of these (11) photographs that Japanese shot from "ChungMinKongKeicho" in 1928 turned out to be completely identical to those of the original copy. The point that Korean History Compilation Committee added the 12 books to Jangkei as referring to the book as "One Keicho(啓草) partially copied(抄寫) in separation" and that Cho Sung-do categorized the 12 books into a supplement and others can be solid proofs to make the Jangkei supplement called "ChungMinKongKeicho". In terms of "ChungMooKongKeicho", since it consists of 62 books in total, it is not reasonable to see the book as Jangkei supplement which has the extra 12 more books for itself. "ChungMooKongKeibon" in "ChungMooKongYusa" was written with a total of 16 books. In the body, Yidumun is only clearly present, and the three books in the later part are same with the original copy of "ChungMooKongKeicho". "YimjinJangcho" by Korean History Compilation Committee has been the only book in which Yidumun was observed so far but now, it is assumed that the publication date of "ChungMooKongKeibon" goes before that of the former. The counterargument to the opinion that "ChungMinKongKeicho" is the supplement to Jangkei is based on Lee Eun-sang's comment "One page of a log in the Jangkei copy supplement." At first Seol Ui-sik introduced a piece photo of the rough draft of "MoosulIlki" in a drawing form through "Nanjung Ilkicho by Yi Sun-sin" in 1953. Lee Eun-sang also added two pages of the handwritten Yilkicho in the Jangkeichobon supplement to "MoosulIlki" and for the second time, the phrase "One page of a log written during the last 10 days after the Jangkei copy supplement" and "Supplement" were used. Those views are originated from the comment "One photograph of the rough draft of "MoosulIlki"" which Seol Ui-sik introduced without knowledge of the exact source. Lee Eun-sang said, "One page of a log in the Jangkei copy supplement" because Lee mistook "ChungMooKongYusa" for a book related to Jangkei. Since it is the wrong argument different from the actual situation of the original copy, if it has to be corrected, it should be rephrased "One page of a log in ChungMooKongYusa." After all, the source of the counterargument is the mistake because there has never been the Jangkei supplement with one page of a log included. All the Jangkeis other than "YimjinJangcho" can be said as the Jangkei supplements but still, they are separated from the other Jangkeis for the extra 12 more books are present in the commonly-called Jangkei supplement. Due to that reason, the argument on how "ChungMinKongKeicho" with the 12 books added is the popular Jangkei supplement should be considered more reasonable.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.