• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.429-438
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• 2021

Owing to the development of science technology, particularly the smart concept and defense policy factors of the 4th industry, military weapon systems are advanced, and the scientific and operational force is reduced dramatically. The aspect of the future war is characterized by the operation of troops with reduced forces from advanced and scientific weapon systems in an operational area that has expanded more than four times compared to the present. Reflecting on these situational factors, it is necessary to improve combat methods based on the changes in the battlefield environment and advanced weapon systems. In this study, to find a more efficient future combat method in a changing war pattern, this study applied the battle experiment methodology using Vision21 war game model, which is an analytical model used by the army. Finally, this study aimed to verify the future combat method and unit structure. Therefore, the scenario composition and experiment method that reflect the change in the ground operational environment and weapon system was first composed. Subsequently, an analysis method based on the combat effectiveness was applied to verify the effective combat performance method and unit structure of future infantry units.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Journal of the Korean Geographical Society
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• v.30 no.4
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• pp.364-388
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• 1995

Even though rarely recognized as a separate approach, the institutional approach in geography is defined as an attempt to seek an explanation of the phenomena of geographical interest through focusing on the effects of institutional structure and actions. It can provide interdisciplinary links with other works and can offer complementary explanations for geographical interests. The concerns for institutions in geography can be found in early studies, the study of the state, the managerial approach, and the regulation approach. The Korean financial systems can provide good examples to apply institutional concepts into creating spatial outcomes, as it has been regarded as a useful tool to promote Korean economic development. Behind the current spatial pattern of financial systems, four different stages of institutional changes are identified in Korea. Each stage has different institutional features reflecting unique spatial implication.

• (The) Research of the performance art and culture
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• no.39
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• pp.393-413
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• 2019

As one of a shaman song to reveal features of the byeolsin rite, East Sea coast, Gyeongbuk, Korea is matgut have been discussed for a long time. The rite is held to greet other shamans who were invited by a village resident and the highest shaman to perform the rite. When there is no highest shaman in the village, or when the highest shaman lacks the ability to perform the exorcism, other shamans are invited if a person is required to meet the scale of the rite, which is a rite of passage to verify their abilities and to become a community, even while the byeolsingut is held. The best shaman team, or village Pungmul team, and shaman team, When the strings are between them, they play music and struggle with each other, and the strings are lifted, they greet the god of the game at the altar, and the shaman turns around and plays with the field. However, the process of greeting the shaman at the exorcism rite after 2000 and the procedure of "Paggi-bogi" in which shamans take turns singing songs are disappearing. In addition, although the village Pungmul team has participated in the rite as a welcoming party for the shaman team, the purpose of the rite, which is to greet the shaman, is also being tarnished by leaving the role of the village Pungmul team to the outside team. This is due to changes in the villagers' perception of gut and shaman. As in the old days, it was necessary to greet a shaman as a rite of worship, although it was not necessary to confirm its ability to perform the rite of the village, due to the disparity of status. However, after the shaman has been transformed from a 'mysterious' existence into an intangible cultural asset functional owner, the perception of shaman changes and the village confirms the shaman's ability through an intangible cultural asset certificate. In addition, the number of villagers who know the details of the rite is decreasing, and most of the tasks such as preparation and proceedings of the rite are delegated to the shaman to hold the byeolsingut, and the nature of the exorcism of the exorcism is changing.

• 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.