• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.489-493
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• 2020

The technological development of the Fourth Industrial Revolution is accelerating the development of the world and society. Fourth industrial technology is connected to Cyber World to develop national and social infrastructure. The application of the fourth industrial technology can create Cyber-city and Cyber-life systems. In the ICT business industry that connects Cyber with the real world, we need to connect Cyber with real-life work. It is to use AR business cards as one of the ways to connect Cyber business with real business. When an AR business card is recognized as a smartphone, a 3D character appears to introduce the main character of the business card. In addition, AR business cards are reflected in AR for product and service of business operators, enabling promotion of products, services, etc. Using connected wired and wireless 5G communication, real-time e-commerce is also possible. In this paper, AR business card will be produced with Source Coding and AR business card platform will be designed.

• Asia-Pacific Journal of Business
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• v.9 no.4
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• pp.49-65
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• 2018

This paper illustrates the competition between the mobile pay and credit card systems by utilizing the theory of two-sided markets. Two firms, as platforms, maximize the profit collecting fees from consumers on one side and from retailers on the other side. Consumers pay to buy goods and services with mobile pay, credit card, or cash. The basic model is one that each platform maximizes its profit. We show that the fees for credit card holders and retailers are higher than the respective costs. The fee for retailers of the mobile payment is higher than its cost, while the buyer's fee may be higher or lower than its cost. Applied model is the one that employs the delegation game model. The total profit of the mobile pay system is composed of its profit and the weighted demand for the mobile pay. It is shown that buyers' fee under the applied model is lower than that under the basic model, resulting in an increase of the demand for the mobile pay. The fee for the retailers rises, albeit the sum of fees for the buyers and retailers falls. The profit for the mobile pay system is increased, while that for the credit card company stays the same.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.8-13
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• 2019

Recently, In line with the recent trend of the fourth industrial revolution, many companies and institutions have been increasingly applying automated technologies using artificial intelligence to various tasks. Particularly, due to the government's 52-hour workweek system, companies are increasingly struggling with manpower management. Therefore, they are interested in RPA (Robotic Process Automation) for office environment automation for efficient manpower management. It is being introduced in the back-office business in credit card companies, bank, insurance. These RPA solutions require AI-based recognition technology, scripting technology, business software API-related technologies, and various solutions such as Automate One, Automation Anywhere, UiPath, and Blue Prism are provided. This paper analyzes and describes the technology of RPA solution, the market trend, and the efficiency of RPA adoption.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.259-266
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• 2023

It is very difficult to measure the performance of the machine learning model in the business service stage. Therefore, managing the performance of the model through the operational department is not done effectively. Academically, various studies have been conducted on the concept drift detection method to determine whether the model status is appropriate. The operational department wants to know quantitatively the performance of the operating model, but concept drift can only detect the state of the model in relation to the data, it cannot estimate the quantitative performance of the model. In this study, we propose a performance prediction model (PPM) that quantitatively estimates precision through the statistics of concept drift. The proposed model induces artificial drift in the sampling data extracted from the training data, measures the precision of the sampling data, creates a dataset of drift and precision, and learns it. Then, the difference between the actual precision and the predicted precision is compared through the test data to correct the error of the performance prediction model. The proposed PPM was applied to two models, a loan underwriting model and a credit card fraud detection model that can be used in real business. It was confirmed that the precision was effectively predicted.