• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.25-38
• /
• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Korean Journal of Heritage: History & Science
• /
• v.49 no.1
• /
• pp.4-21
• /
• 2016

The purpose of this study is to investigate changes of Jinju Sword Dance, characteristics of the changes, and the current condition of its preservation and succession after the designation as the important intangible cultural property no. 12 in January 16th, 1967. In other words, this study understands the situation which has established the present state of after changes over generations. As of now. the year of 2015, the 3 generation holders have been approved since 1967. In 1967, 8 members of $1^{st}$ generation holders were selected from gisaengs of Gwonbeon. However, the succession training was incomplete due to conflicts among the holders, the deaths of some holders, and economic activities of the individuals. As the need of a pivot for succession training and activities was rising, Seong, Gye-Ok was additionally approved as the $2^{nd}$ generation holder on June $21^{st}$, 1978. Seong, Gye-Ok who had never been a gisaeng had dramatically changed with a lot of new attempts. After the death of Seong, Gye-Ok in 2009, Kim, Tae-Yeon and Yu, Yeong-Hee were approved as the $3^{rd}$ generation holders in February, 2010. Based on the resources including the "Cultural Research Reports of Important Intangible Cultural Properties" in 1966 and videos up to 2014, the changes of the dance and surroundings are as follow. 1. The formation of musical accompaniment has been changed during the 3 generations. In the video of the $1^{st}$ generation(in 1970), the performance lasted about 15 minutes, whereas the performance lasted 25 minutes in the video of the $2^{nd}$ generation. Yumbuldoduri rhythm was considered as Ginyumbul(Sangryeongsan) and played more slowly. The original dance requiring only 15 rhythms was extended to 39 rhythms to provide longer performance time. In the $3^{rd}$ generation, the dance recovered 15 rhythms using the term Ginyumbul. The facts that Yumbul was played for 3 minutes in the $1^{st}$ generation but for 5 minutes in the 3rd generation shows that there was tendency pursuing the slowness from the $2^{nd}$ generation. 2. For the composition of the Dance, the performance included additional 20 rhythms of Ginyumbul and Ah(亞)-shaped formation from the $2^{nd}$ generation. From the $3^{rd}$ generation, the performance excluded the formation which had no traditional base. For the movement of the Dance, the bridge poses of Ggakjittegi and Bangsukdoli have been visibly inflexible. Also, the extention of time value in 1 beat led the Dance less vibrant. 3. At the designation as an important intangible cultural property (in 1967), the swords with rotatable necks were used, whereas the dancers had been using the swords with non-rotatable necks since late 1970s when the $2^{nd}$ generation holder began to used them. The swords in the "Research Reports" (in 1966) was pointy and semilunar, whereas the straight swords are being used currently. The use of the straight swords can be confirmed from the videos after 1970. 4. There is no change in wearing Jeonlib, Jeonbok, and Hansam, whereas the arrangement of Saekdong of Hansam was different from the arrangement shown in the "Research Reports". Also, dancers were considered to begin wearing the navy skirts when the swords with non-rotatable necks began to be used. Those results showed that has been actively changed for 50 years after the designation. The $2^{nd}$ generation holder, Seong, Gye-Ok, was the pivot of the changes. However, , which was already designated as an important intangible cultural property, is considered to be only a victim of the change experiment from the project to restore Gyobang culture in Jinju, and it is a priority to conduct studies with historical legitimacy. First of all, the slowing beat should be emphasized as the main fact to reduce both the liveliness and dynamic beauty of the Dance.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.85-107
• /
• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• The Journal of Korean Philosophical History
• /
• no.35
• /
• pp.309-334
• /
• 2012

This study attempts to study in what form Folk Taoism in the late of $Chos{\breve{o}}n$ Dynasty has existed and discuss the contents and characteristics of ideological aspects forming the foundation of private Taoism. While Guan Yu Belief(關帝信仰) in the late of $Chos{\breve{o}}n$ Dynasty is a folk belief focusing on Guan Yu, Seoneumjeulgyo(善陰?敎) and Musangdan(無相壇) are religious groups with organization. In case of Seoneumjeulgyo(善陰?敎), 'Seoneumjeul' contains perspective of Tian(天觀) of Confucianism but the ascetic practice method is to practice by reciting the name of the Buddha and the targets of a belief are Gwanje, Munchang, Buwoo. This shows the unified phenomenon of Confucianism, Buddhism, Taoism of Folk Taoism in the late of $Chos{\breve{o}}n$ Dynasty. Guan Yu Belief started at the national level led by the royal family of $Chos{\breve{o}}n$ after Japanese Invasion of Korea in 1592 was firmly settled in non-official circles. Guan Yu in the late of $Chos{\breve{o}}n$ Dynasty is expressed as the incarnation of loyalty and filial piety as well as God controlling life, death and fate. As this divine power and empowerment were spreading as scriptures among people, Guan Yu Belief was settled as a target to defeat the evil and invoke a blessing. Seoneumjeulgyo is the religious group that imitated 'Paekryunsa(白蓮社)' of Ming Qing time of China. Seoneumjeulgyo emphasized 'sympathy' with God through chanting. And it expressed writing written in the state of religious ecstasy as 'Binan(飛鸞).' Binan is also called as revelation and means to be revealed from heaven in the state united with God. Seoneumjeulgyo pursued the state united with God through a recitation of a spell and made scriptures written in the state united with God as its central doctrine. Musangdan published and spread Nanseo(鸞書,Book written by the revelation from God) and Seonso(善書) while worshipping Sam Sung Je Kun(三聖帝君). The scriptures of Folk Taoismin the late of $Chos{\breve{o}}n$ Dynasty can be roughly divided into Nanseo(鸞書) and Seonso(善書). Nanseo is a book written by the revelation from God and Seonso is a book to the standards of good deeds and encourage a person to do them such as Taishangganyingbian(太上感應篇) and Gonghwagyuk(功過格). The characteristics of Folk Taoism in the late of $Chos{\breve{o}}n$ Dynasty are as follows. First, a shrine of Guan Yu built for political reasons played a central role of Folk Taoism in the late of $Chos{\breve{o}}n$ Dynasty. Second, specific private Taoist groups such as Temple $Myory{\breve{o}}nsa$ and Musangdan appeared in the late of $Chos{\breve{o}}n$ Dynasty. These are Nandan Taoism(鸞壇道敎) that pursued the unity of God through 'sympathy' with God. Third, private Taoism of $Chos{\breve{o}}n$ was influenced by the unity of Confucianism, Buddhism, Taoism with private Taoism in the Qing Dynasty of China and religious organization form etc. Fourth, the Folk Taoism scriptures of $Chos{\breve{o}}n$ are divided into Nanseo and Seonso and Nanseo directly made in $Chos{\breve{o}}n$ is expected to be the key to reveal the characteristics of Folk Taoism.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.27-65
• /
• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

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

• Journal of the Daesoon Academy of Sciences
• /
• v.35
• /
• pp.217-253
• /
• 2020

This study analyzes the principles of the 'Earthly Paradise' (仙境, the realm of immortals), 'Virtuous Concordance of Yin and Yang' (陰陽合德), and the 'Reordering Works of Heaven and Earth' (天地公事) while combining them with Joseon art. Therefore, this study aims to discover the context wherein the concept of Taiji in 'Daesoon Truth,' deeply penetrates into Joseon art. Doing so reveals how 'Daesoon Thought' is embedded in the lives and customs of the Korean people. In addition, this study follows a review of the sentiments and intellectual traditions of the Korean people based on 'Daesoon Thought' and creative works. Moreover, 'Daesoon Thought' brings all of this to the forefront in academics and art at the cosmological level. The purpose of this research is to vividly reveal the core of 'Daesoon Thought' as a visual image. Through this, the combination of 'Daesoon Thought' and Joseon art will secure both data and reality at the same time. As part of this, this study deals with the world of 'Daesoon Thought' as a cosmological Taiji principle. This concept is revealed in Joseon art, which is analyzed and examined from the viewpoint of art philosophy. First, as a way to make use of 'Daesoon Thought,' 'Daesoon Truth' was developed and directly applied to Joseon art. In this way, reflections on Korean life within 'Daesoon Thought' can be revealed. In this regard, the selection of Joseon art used in this study highlights creative works that have been deeply ingrained into people's lives. For example, as 'Daesoon Thought' appears to focus on the genre painting, folk painting, and landscape painting of the Joseon Dynasty, attention is given to verifying these cases. This study analyzes 'Daesoon Thought,' which borrows from Joseon art, from the perspective of art philosophy. Accordingly, attempts are made to find examples of the 'Virtuous Concordance of Yin and Yang' and Tai-Ji in Joseon art which became a basis by which 'Daesoon Thought' was communicated to people. In addition, appreciating 'Daesoon Thought' in Joseon art is an opportunity to vividly examine not only the Joseon art style but also the life, consciousness, and mental world of the Korean people. As part of this, Chapter 2 made several findings related to the formation of 'Daesoon Thought.' In Chapter 3, the structures of the ideas of 'Earthly Paradise' and 'Virtuous Concordance of Yin and Yang' were likewise found to have support. And 'The Reordering Works of Heaven and Earth' and Tai-Ji were found in depictions of metaphysical laws. To this end, the laws of 'The Reordering Works of Heaven and Earth' and the structure of Tai-Ji were combined. In chapter 4, we analyzed the 'Daesoon Thought' in the life and work of the Korean people at the level of the convergence of 'Daeesoon Thought' and Joseon art. The analysis of works provides a glimpse into the precise identity of 'Daesoon Thought' as observable in Joseon art, as doing so is useful for generating empirical data. For example, works such as Tai-Jido, Ssanggeum Daemu, Jusachaebujeokdo, Hwajogi Myeonghwabundo, and Gyeongdodo are objects that inspired descriptions of 'Earthly Paradise', 'Virtuous Concordance of Yin and Yang,' and 'The Reordering Works of Heaven and Earth.' As a result, Tai-Ji which appears in 'Daesoon Thought', proved the status of people in Joseon art. Given all of these statements, the Tai-Ji idea pursued by Daesoon Thought is a providence that follows change as all things are mutually created. In other words, it was derived that Tai-Ji ideology sits profoundly in the lives of the Korean people and responds mutually to the providence that converges with 'Mutual Beneficence.'

• Journal of the Daesoon Academy of Sciences
• /
• v.36
• /
• pp.1-34
• /
• 2020

It has been suggested that Jeungsan's prophetic poem that starts with the verse "For about seven or eight years, there will be a castle in the ancient country [七八年間古國城] ⋯" originally comes from Prophecies of Jeong Gam (鄭鑑錄). Despite Jeungsan, himself, obviously having been critical of that text, this claim has become the basic grounds for discourse suggesting that Jeungsan was not only interested in Prophecies of Jeong Gam but also considerably influenced by the text. However, the claim itself was formulated due to misunderstandings of the Chinese poems that had been included in A Compilation of Secret Prophecies Hidden in the Family-clan of Seogye (西溪家臧訣). These poems pursue a different ideological orientation than the poem from Prophecies of Jeong Gam. Ultimately, the Chinese poem in the verse 84 the chapter titled, Prophetic Elucidations in The Canonical Scripture of Daesoon Jinrihoe cannot provide a basis for the claim that Jeungsan was strongly influenced by Prophecies of Jeong Gam. This claim that Prophecies of Jeong Gam made a deep impact on Jeungsan and Daesoon Thought was based on three other texts outside of those that appear within verse 84 of Prophetic Elucidations. The first supposedly-related line is: "Heaven opens at the period of the Rat (Ja 子), Earth opens at the period of the Ox (Chuk 丑), humankind starts at the period of the Tiger (Ihn 寅)." This line comes from from Shao Kangjie's Book of Supreme World Ordering Principles (皇極經世), and the line could be quoted idiomatically as an expression in the Joseon Dynasty. Accordingly, attempts to relate Daesoon Thought to Prophecies of Jeong Gam are a distortion that arise from the assumption that Jeungsan had a significant interest in Prophecies of Jeong Gam. The second related line is "At the foot of Mount Mother (母岳山), a golden icon of Buddha has the ability to speak [母岳山下 金佛能言]." That line is nearly identical to the verse "On the summit of Mount Mother, a golden icon of Buddha has the ability to speak [母岳山頭 金佛能言]." Yet, Jeungsan changed '頭 (du, the summit)' to '下 (ha, the foot or under)' and express his own unique religious prophecy. This allusion to the prophecies of Jeong Gam is actually a criticism designed to disprove the earlier prophecy. Third, is the verse, "The form of Buddhism, creation of daoism, and propriety of Confucianism [佛之形體仙之造化儒之凡節]," which is characteristically related to Daesoon Thought. This verse can only be found in the prophetic text, Prophecies of Chochang (蕉蒼訣), and it is provided a main source when alleging that Prophecies of Jeong Gam was an influence on Daesoon Thought. However, considering the context of Prophecies of Chochang and the year of its publication (it is assumed to be compiled after 1950s), this does not hold water as Jeungsan had already passed into Heaven several decades before that time. This disqualifies the verse from being a basis for asserting Prophecies of Jeong Gam as an influence on Daesoon Thought. Contrary to the original assertion, there is a considerable amount of evidence that Prophecies of Chochang absorbed aspects of Daesoon Thought, which were simply revised in a novel way. There is no truly compelling evidence underpinning the argument that Prophecies of Jeong Gam had a unilateral impact on Daesoon Thought. There seems to be a great deal of confusion and numerous misinterpretations on this matter. Therefore, the claim that Daesoon Thought, as developed by Jeungsan, was influenced by the discourse on dynastic revolution and feng shui contained in Prophecies of Jeong Gam should be re-examined at the level of its very premise.

• Trans-
• /
• v.12
• /
• pp.51-79
• /
• 2022

This study investigates the methods for deriving colors which can serve as a reference to users such as designers and or contents creators who search for online images from the web portal sites using specific words for color planning and more. Two experiments were conducted in order to accomplish this. Digital scenery photos within the geographic scope of Korea were downloaded from web portal sites, and those photos were studied to find out what colors were used to describe daytime and nighttime. Machine learning was used as the study methodology to classify colors in daytime and nighttime, and KSCA was used to derive the color frequency of daytime and nighttime photos and to compare and analyze the two results. The results of classifying the colors of daytime and nighttime photos using machine learning show that, when classifying the colors by 51~100%, the area of daytime colors was approximately 2.45 times greater than that of nighttime colors. The colors of the daytime class were distributed by brightness with white as its center, while that of the nighttime class was distributed with black as its center. Colors that accounted for over 70% of the daytime class were 647, those over 70% of the nighttime class were 252, and the rest (31-69%) were 101. The number of colors in the middle area was low, while other colors were classified relatively clearly into day and night. The resulting color distributions in the daytime and nighttime classes were able to provide the borderline color values of the two classes that are classified by brightness. As a result of analyzing the frequency of digital photos using KSCA, colors around yellow were expressed in generally bright daytime photos, while colors around blue value were expressed in dark night photos. For frequency of daytime photos, colors on the upper 40% had low chroma, almost being achromatic. Also, colors that are close to white and black showed the highest frequency, indicating a large difference in brightness. Meanwhile, for colors with frequency from top 5 to 10, yellow green was expressed darkly, and navy blue was expressed brightly, partially composing a complex harmony. When examining the color band, various colors, brightness, and chroma including light blue, achromatic colors, and warm colors were shown, failing to compose a generally harmonious arrangement of colors. For the frequency of nighttime photos, colors in approximately the upper 50% are dark colors with a brightness value of 2 (Munsell signal). In comparison, the brightness of middle frequency (50-80%) is relatively higher (brightness values of 3-4), and the brightness difference of various colors was large in the lower 20%. Colors that are not cool colors could be found intermittently in the lower 8% of frequency. When examining the color band, there was a general harmonious arrangement of colors centered on navy blue. As the results of conducting the experiment using two methods in this study, machine learning could classify colors into two or more classes, and could evaluate how close an image was with certain colors to a certain class. This method cannot be used if an image cannot be classified into a certain class. The result of such color distribution would serve as a reference when determining how close a certain color is to one of the two classes when the color is used as a dominant color in the base or background color of a certain design. Also, when dividing the analyzed images into several classes, even colors that have not been used in the analyzed image can be determined to find out how close they are to a certain class according to the color distribution properties of each class. Nevertheless, the results cannot be used to find out whether a specific color was used in the class and by how much it was used. To investigate such an issue, frequency analysis was conducted using KSCA. The color frequency could be measured within the range of images used in the experiment. The resulting values of color distribution and frequency from this study would serve as references for color planning of digital design regarding natural scenery in the geographic scope of Korea. Also, the two experiments are meaningful attempts for searching the methods for deriving colors that can be a useful reference among numerous images for content creator users of the relevant field.

• Asia Marketing Journal
• /
• v.14 no.1
• /
• pp.131-157
• /
• 2012

Despite the voluminous research on switching barriers, the notion that they can generate negative responses has not been investigated. Further, a critical question is what determines the strength of such negative responses. To address this question, the classic theory of psychological reactance is briefly reviewed, and the idea of switching barrier is advanced. This study attempts to suggest a model on the negative effects of switching- frustrated situation, based on the studies on psychological reactance. According to psychological reactance theory(Brehm 1966), whenever a freedom is threatened or removed, individuals are motivated, at least temporarily, to restore their freedom. For example, if individuals think they are free to engage in behaviors .v, y, or z, then threatening their freedom to engage in x would cause psychological reactance. This reactance could be reduced by an increase in the perceived attractiveness of engaging in, the threatened behavior(Kivetz 2005). This investigation seeks to extend existing switching barrier research in three important ways. First, while the past research has emphasized only positive role of switching barrier, this study address negative role of it by applying psychological reactance theory. Second, to find negative results of switching barrier, I suggest negative psychological response including regret to the past choice, resentment to the present provider, and strong desire to the alternative provider. Third, I suggest the perceived severity of the switching barriers, the attractiveness of the alternative as switching-frustrated situation which can lead to negative results. And, in addition to these relationships, I added moderated effects of perceived justice for better explanation. So this study includes the following hypotheses. H1-1 ~ H1-3: The attractiveness of the alternative has a positive effect regret to the past choice (h1-1), resentment to the present provider (h1-2), and strong desire to the alternative provider (h1-3). H2-1 ~ H2-3 : The perceived severity of the switching barrier has a positive effect regret to the past choice (h2-1), resentment to the present provider (h2-2), and strong desire to the alternative provider (h2-3). H3-1 ~ H3-3 : The positive relationships between the attractiveness of the alternative and consumer' negative responses will be stronger at low level of perceived justice than at high level of perceived justice. H4-1 ~ H4-3 : The positive relationships between the perceived severity of the switching barrier and consumer' negative responses will be stronger at low level of perceived justice than at high level of perceived justice. Survey research is employed to test hypotheses involving perceived severity of the switching barrier(Hess 2008), attractiveness of the alternative(Anderson and Narus 1990; Ohanian 1990),regret(Glovich and Medvec 1995), resentment, strong desire(Alcohol Urge Questionaire: Bohn et al. 1995), perceived justice(Bies and Moag 1986; Clemmer 1993; Lind and Tyler 1998). Previous researches, such as reactance theory, emotion and service failure, have been referenced to measure constructs. All items were measured on a 7-point Likert scale ranging from "strongly disagree" to "strongly agree". We collected data involving various service field, and used 249 respondents to analyze these data using the moderated regression. The results of our analysis suggest, as expected, that the perceived severity of the switching barrier had positive effects on regret to the past choice(b = .197, p< .01), resentment to the present provider(b = .214, p< .01), and strong desire to the alternative provider(b = .254, p< .001). And the attractiveness of the alternative had positive effects on regret to the past choice(b = .353, p<.001), resentment to the present provider(b = .174, p< .01), and strong desire to the alternative provider(b = .265, p< .001). However, our findings indicate perceived justice partly moderates relationship between switching-frustrated situation and psychological negative response. The study has brought to light a number of insights between switching barriers and consumer' negative responses that have been subject to little prior research. In particular, this study adds to the existing understanding of the psychological responses to switching barriers in switching- frustrated situation. This research therefore has significance to marketers for strategic marketing programs, particularly in terms of customer retention and switching barrier strategies. Since consumers could exhibit negative responses to switching barrier, companies would be able to lose their customer when they thoughtlessly use switching barrier for remaining customer. Although the study has these contributions, there are several limitations including unsupported hypotheses and research method. So, we need to make up for these limitations in the future researches.