• Journal of Information Processing Systems
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• v.17 no.3
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• pp.599-614
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• 2021

This paper proposes a digital signage system based on an intelligent recommendation model. The proposed system consists of a server and an edge. The server manages the data, learns the advertisement recommendation model, and uses the trained advertisement recommendation model to determine the advertisements to be promoted in real time. The advertisement recommendation model provides predictions for various products and probabilities. The purchase index between the product and weather data was extracted and reflected using correlation analysis to improve the accuracy of predicting the probability of purchasing a product. First, the user information and product information are input to a deep neural network as a vector through an embedding process. With this information, the product candidate group generation model reduces the product candidates that can be purchased by a certain user. The advertisement recommendation model uses a wide and deep recommendation model to derive the recommendation list by predicting the probability of purchase for the selected products. Finally, the most suitable advertisements are selected using the predicted probability of purchase for all the users within the advertisement range. The proposed system does not communicate with the server. Therefore, it determines the advertisements using a model trained at the edge. It can also be applied to digital signage that requires immediate response from several users.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.240-242
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• 2021

Recently, Kiosks, a wireless ordering system, has been introduced as the system where employees and customers order in person has disappeared in most stores. However, Drive-Thru, which is serviced in stores, still has a system where employees and customers communicate directly to order. In this paper, we propose the design of a Drive-Thru system in which customers order direct communication between Drive-Thru's employees and customers without facing them using QR-Code. The non-face-to-face Drive-Thru system aims to provide a more automated system by minimizing employees in the store. As a result, customers receive more accurate services and have the effect of improving the quality of consumption.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.390-392
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• 2018

We occasionally spend a lot of time looking for things after we forget where we store things. In this paper, we design and develop a product management system that keeps things, tells them where they are, and makes them easy to find. When a person puts a thing on the robot and selects the name of the object through the robot control application installed on the smartphone, the user confirms the empty storage place, moves it, stores it,. Through this paper, it is expected that we will be able to easily find the things we manage at home without forgetting, and contribute to the development of unmanned warehouse management technology.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.100-105
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• 2021

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.179-188
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• 2024

Recently, unmanned terminals (kiosks) have greatly increased their scope of use by allowing orders or payments to be made efficiently while replacing existing manpower. With the acceleration of digital transformation, the rate of kiosk installations has soared, making it an important task to help all age groups easily use kiosks. In this study, based on the function of the game engine and Hand Tracking technology, we developed content that can conduct kiosk education in a virtual world. The goal of this content is to help users unfamiliar with kiosks confidently cope with real-world situations by experiencing visual and auditory stress caused by waiting guests in virtual reality. To this end, two VR contents were developed. One is a simple kiosk experience content, and the other is specialized content that causes visual and auditory stress. The experiment was divided into two groups: one group experienced general VR content, while the other group experienced specialized VR content. After the experience, both groups proceeded to place orders in an actual store. Survey results showed that 85% of the experimental group and 95% of the control group were satisfied with the training. This confirms that the specialized experience described in this paper is effective for kiosk education.