• (The)Study of the Eastern Classic
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• no.72
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• pp.365-400
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• 2018

The signs of $G{\bar{a}}nzh{\bar{i}}$(干支: the sexagesimal calendar system) almanac, which marked each year, month, day and time with 60 ordinal number marks made by combining 10 $Ti{\bar{a}}ng{\bar{a}}ns$(天干: the decimal notation to mark date) and 12 $D{\grave{i}}zh{\bar{i}}s$(地支 : the duodecimal notation to mark date), were used not only as the sign of the factors affecting the occurrence of a disease and treatment in the area of traditional oriental medicine, but also as the indicator of prejudging fortunes in different areas of future prediction techniques.(for instance, astrology, the theory of divination based on topography, four pillars of destiny and etc.) While theories of many future predictive technologies with this $G{\bar{a}}nzh{\bar{i}}$(干支) almanac signs as the standard had been established in many ways by Han dynasty, it is difficult to find almanac discussion later on the fundamental theory of 'how it works like that'. As for the method to mark the era of $Ti{\bar{a}}nt{\check{i}}l{\grave{i}}$(天體曆: a calendar made with the sidereal period of Jupiter and the Sun), which determines the name of a year depending on where $Su{\grave{i}}x{\bar{i}}ng$(歲星: Jupiter) is among the '12 positions of zodiac', there are three main ways of $$Su{\grave{i}}x{\bar{i}}ng-J{\grave{i}}ni{\acute{a}}nf{\check{a}}$$(歲星紀年法: the way to mark an era by the location of Jupiter on the celestial sphere), $$T{\grave{a}}isu{\grave{i}}-J{\grave{i}}ni{\acute{a}}nf{\check{a}}$$ (太歲紀年法: the way to mark an era by the location facing the location of Jupiter on the celestial sphere) and $$G{\bar{a}}nzh{\bar{i}}-J{\grave{i}}ni{\acute{a}}nf{\check{a}}$$(干支紀年法: the way to mark an era with Ganzhi marks). Regarding $$G{\bar{a}}nzh{\bar{i}}-J{\grave{i}}ni{\acute{a}}nf{\check{a}}$$(干支紀年法), which is actually the same way to mark an era as $$T{\grave{a}}isu{\grave{i}}-J{\grave{i}}ni{\acute{a}}nf{\check{a}}$$(太歲紀年法) with the only difference in the name, there are more than three ways, and one of them has continued to be used in China, Korea and so on since Han dynasty. The name of year of $G{\bar{a}}nzh{\bar{i}}$(干支) this year, 2018, has become $W{\grave{u}}-X{\bar{u}}$(戊戌) just by 'accident'. Therefore, in this discussion, the need to realize this situation was emphasized in different areas of traditional techniques of future prediction in which distinct theories have been established with the $G{\bar{a}}nzh{\bar{i}}$(干支) mark of year, month, day and time. Because of the 1 sidereal period of Jupiter, which is a little bit shorter than 12 years, once about one thousand years, 'the location of Jupiter on the zodiac' and 'the name of a year of 12 $D{\grave{i}}zh{\bar{i}}s$(地支) marks' accord with each other just for about 85 years, and it has been verified that recent dozens of years are the very period. In addition, appropriate methods of observing the the twenty-eight lunar mansions were elucidated. As $G{\bar{a}}nzh{\bar{i}}$(干支) almanac is related to the theoretical foundation of traditional medical practice as well as various techniques of future prediction, in-depth study on the fundamental theory of ancient $Ti{\bar{a}}nt{\check{i}}l{\grave{i}}$(天體曆) cannot be neglected for the succession and development of traditional oriental study and culture, too.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.1-21
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• 2021

As the global business environment changes, uncertainties in technology development and market needs increase, and competition among companies intensifies, interests and demands for R&D activities of individual companies are increasing. In order to cope with these environmental changes, R&D companies are strengthening R&D investment as one of the means to enhance the qualitative competitiveness of R&D while paying more attention to facility investment. As a result, facilities or R&D investment elements are inevitably a burden for R&D companies to bear future uncertainties. It is true that the management strategy of increasing investment in R&D as a means of enhancing R&D capability is highly uncertain in terms of corporate performance. In this study, the structural factors that influence the R&D capabilities of companies are explored in terms of technology management capabilities, R&D capabilities, and corporate classification attributes by utilizing data mining techniques, and the characteristics these individual factors present according to the level of R&D capabilities are analyzed. This study also showed cluster analysis and experimental results based on evidence data for all domestic R&D companies, and is expected to provide important implications for corporate management strategies to enhance R&D capabilities of individual companies. For each of the three viewpoints, detailed evaluation indexes were composed of 7, 2, and 4, respectively, to quantitatively measure individual levels in the corresponding area. In the case of technology management capability and R&D capability, the sub-item evaluation indexes that are being used by current domestic technology evaluation agencies were referenced, and the final detailed evaluation index was newly constructed in consideration of whether data could be obtained quantitatively. In the case of corporate classification attributes, the most basic corporate classification profile information is considered. In particular, in order to grasp the homogeneity of the R&D competency level, a comprehensive score for each company was given using detailed evaluation indicators of technology management capability and R&D capability, and the competency level was classified into five grades and compared with the cluster analysis results. In order to give the meaning according to the comparative evaluation between the analyzed cluster and the competency level grade, the clusters with high and low trends in R&D competency level were searched for each cluster. Afterwards, characteristics according to detailed evaluation indicators were analyzed in the cluster. Through this method of conducting research, two groups with high R&D competency and one with low level of R&D competency were analyzed, and the remaining two clusters were similar with almost high incidence. As a result, in this study, individual characteristics according to detailed evaluation indexes were analyzed for two clusters with high competency level and one cluster with low competency level. The implications of the results of this study are that the faster the replacement cycle of professional managers who can effectively respond to changes in technology and market demand, the more likely they will contribute to enhancing R&D capabilities. In the case of a private company, it is necessary to increase the intensity of input of R&D capabilities by enhancing the sense of belonging of R&D personnel to the company through conversion to a corporate company, and to provide the accuracy of responsibility and authority through the organization of the team unit. Since the number of technical commercialization achievements and technology certifications are occurring both in the case of contributing to capacity improvement and in case of not, it was confirmed that there is a limit in reviewing it as an important factor for enhancing R&D capacity from the perspective of management. Lastly, the experience of utility model filing was identified as a factor that has an important influence on R&D capability, and it was confirmed the need to provide motivation to encourage utility model filings in order to enhance R&D capability. As such, the results of this study are expected to provide important implications for corporate management strategies to enhance individual companies' R&D capabilities.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.

• Korean Journal of Environment and Ecology
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• v.37 no.4
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• pp.302-314
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• 2023

This study aims to explore the direction for Korea's effective response to Target 3 (30by30), which can be said to be the core of the Kunming-Montreal Global Biodiversity Framework (K-M GBF) of the Convention on Biological Diversity (CBD), to find the direction of systematic OECM (Other Effective area-based Conservation Measures) discovery at the national level through a survey of global conceptual review and expert perception of OECM. This study examined ① the use of Korean terms related to OECM, ② derivation of determining criteria reflecting global standards, ③ deriving types of potential OECM candidates in Korea, and ④ considerations for OECM identification and reporting to explore the direction for identifying systematic, national-level OECM that complies with global standards and reflects the Korean context. First, there was consensus for using Korean terminology that reflects the concept of OECM rather than simple translations, and it was determined that "nature coexistence area" was the most preferred term (12 people) and had the same context as CBD 2050 Vision of "a world of living in harmony with nature." This study suggests utilizing four criteria (1. No protected areas, 2. Geographic boundaries, 3. Governance/management, and 4. Biodiversity value) that reflect OECM's core characteristics in the first-stage selection process, carrying out the consensus-building process (stage 2) with the relevant agencies, and adding two criteria (3-1 Effectiveness and sustainability of governance and management and 4-1 Long-term conservation) and performing the in-depth diagnosis in stage 3 (full assessment for reporting). The 28 types examined in this study were generally compatible with OECMs (4.45-6.21/7 points, mean 5.24). In particular, the "Conservation Properties (6.21 points)" and "Conservation Agreements (6.07 points)", which are controlled by National Nature Trust, are shown to be the most in line with the OECM concept. They were followed by "Buffer zone of World Natural Heritage (5.77 points)", "Temple Forest (5.73 points)", "Green-belt (Restricted development zones, 5.63 points)", "DMZ (5.60 points)", and "Buffer zone of biosphere reserve (5.50 point)" to have high potential. In the case of "Uninhabited Islands under Absolute Conservation", the response that they conformed to the protected areas (5.83/7 points) was higher than the OECM compatibility (5.52/7 points), it is determined that in the future, it would be preferable to promote the listing of absolute unprotected islands in the Korea Database on Protected Areas (KDPA) along with their surrounding waters (1 km). Based on the results of a global OECM standard review and expert perception survey, 10 items were suggested as considerations when identifying OECM in the Korean context. In the future, continuous research is needed to identify the potential OECMs through site-level assessment regarding these considerations and establish an effective in-situ conservation system at the national level by linking existing protected area systems and identified OECMs.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.45-68
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• 2022

The central and local governments of the Republic of Korea provided information necessary for disaster response through wireless emergency alerts (WEAs) in order to overcome the pandemic situation in which COVID-19 rapidly spreads. Among all channels for delivering disaster information, wireless emergency alert is the most efficient, and since it adopts the CBS(Cell Broadcast Service) method that broadcasts directly to the mobile phone, it has the advantage of being able to easily access disaster information through the mobile phone without the effort of searching. In this study, the characteristics of wireless emergency alerts sent to Seoul during the past year and one month (January 2020 to January 2021) were derived through various text mining methodologies, and various types of information contained in wireless emergency alerts were analyzed. In addition, it was confirmed through the population mobility by age in the districts of Seoul that what kind of influence it had on the movement behavior of people. After going through the process of classifying key words and information included in each character, text analysis was performed so that individual sent characters can be used as an analysis unit by applying a document cluster analysis technique based on the included words. The number of WEAs sent to the Seoul has grown dramatically since the spread of Covid-19. In January 2020, only 10 WEAs were sent to the Seoul, but the number of the WEAs increased 5 times in March, and 7.7 times over the previous months. Since the basic, regional local government were authorized to send wireless emergency alerts independently, the sending behavior of related to wireless emergency alerts are different for each local government. Although most of the basic local governments increased the transmission of WEAs as the number of confirmed cases of Covid-19 increases, the trend of the increase in WEAs according to the increase in the number of confirmed cases of Covid-19 was different by region. By using structured econometric model, the effect of disaster information included in wireless emergency alerts on population mobility was measured by dividing it into baseline effect and accumulating effect. Six types of disaster information, including date, order, online URL, symptom, location, normative guidance, were identified in WEAs and analyzed through econometric modelling. It was confirmed that the types of information that significantly change population mobility by age are different. Population mobility of people in their 60s and 70s decreased when wireless emergency alerts included information related to date and order. As date and order information is appeared in WEAs when they intend to give information about Covid-19 confirmed cases, these results show that the population mobility of higher ages decreased as they reacted to the messages reporting of confirmed cases of Covid-19. Online information (URL) decreased the population mobility of in their 20s, and information related to symptoms reduced the population mobility of people in their 30s. On the other hand, it was confirmed that normative words that including the meaning of encouraging compliance with quarantine policies did not cause significant changes in the population mobility of all ages. This means that only meaningful information which is useful for disaster response should be included in the wireless emergency alerts. Repeated sending of wireless emergency alerts reduces the magnitude of the impact of disaster information on population mobility. It proves indirectly that under the prolonged pandemic, people started to feel tired of getting repetitive WEAs with similar content and started to react less. In order to effectively use WEAs for quarantine and overcoming disaster situations, it is necessary to reduce the fatigue of the people who receive WEA by sending them only in necessary situations, and to raise awareness of WEAs.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (