• Information Systems Review
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• v.23 no.4
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• pp.69-86
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• 2021

As artificial intelligence technology advances, personalized recommendation systems using big data have attracted huge attention. In the case of beauty products, product preferences are clearly divided depending on customers' skin types and sensitivity along with individual tastes, so it is necessary to provide customized recommendation services based on accumulated customer data. Therefore, by employing deep learning methods, this study proposes a neural network-based recommendation model utilizing both product search history and context information such as gender, skin types and skin worries of customers. The results show that our model with context information outperforms collaborative filtering-based recommender system models using customer search history.

• Industry Promotion Research
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• v.5 no.4
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• pp.1-13
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• 2020

Since the 4th industrial revolution was thrown into the world at the Davos World Economic Forum in January 2016, the world has been undergoing major social and economic changes. In this study, the direction of quality management in the 4th industrial revolution era was examined. First, in all the major countries the industrial structural changes and smart business models were confirmed due to the convergence of new ICT such as IoT, robotics, 3D printing, big data, and AI with the existing technologies and industries. Second, we found that although the core technology level of the 4th industrial revolution in Korea is not as good as that of advanced countries, we have been working on expanding smart production methods and creating new industries by utilizing new ICT. Finally, it was confirmed that quality management is a real-time implementation of new ICT that reflects the needs of the market in real time based on big data from the planning and design stage of products or services.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.31-37
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• 2021

Recently, in the market of home appliances, the technical differentiation of products using convergence technology has been receiving a lot of response to satisfy consumer demand. However, air-conditioner products are an area that requires research and development in the early stages of convergence technology. In this paper, it is developed that a non-contact bio-signal(respiration, movement) collection technology using IR-UWB(Impulse-Radio Ultra Wideband) technology, which controls the air-conditioner direction according to the user's location and also monitors sleep to provide an optimal sleep environment. In addition, emotional lighting and ASMR are developed to provide a comfortable and emotional place of life. Finally, based on the developed convergence technology, we develop intelligent smart air-conditioning services for the convenience of daily life and a comfortable resting space.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.21-28
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• 2023

In manufacturing companies that focus on small-scale production of multiple product varieties, defective products are manually selected by workers rather than relying on automated inspection. Consequently, there is a higher risk of incorrect sorting due to variations in selection criteria based on the workers' experience and expertise, without consistent standards. Moreover, for non-standardized flexible objects with varying sizes and shapes, there can be even greater deviations in the selection criteria. To address these issues, this paper designs a quality inspection system using artificial intelligence-based unsupervised learning methods and conducts research by experimenting with accuracy using a dataset obtained from real manufacturing environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.463-465
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• 2021

Currently, the inspection method printed on food packages and boxes is to sample only a few products and inspect them with human eyes. Such a sampling inspection has the limitation that only a small number of products can be inspected. Therefore, accurate inspection using a camera is required. This paper proposes a deep learning object recognition technology model, which is an artificial intelligence technology, as a method for detecting the defects of expiration date printed on the product packaging. Using the Faster R-CNN (region convolution neural network) model, the color images, converted gray images, and converted binary images of the printed expiration date are trained and then tested, and each detection rates are compared. The detection performance of expiration date printed on the package by the proposed method showed the same detection performance as that of conventional vision-based inspection system.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.17-25
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• 2020

Based on the successful use of chatbot technology, this study examined what business values each company is creating. The chatbot service contributes to improving the productivity of the company by helping to answer or respond to the questions of employees inside the company or customers. And in the field of education, Instead of instructor, AI technology responds the questions and feedback of the students to reduce the work of the instructor. In the field of commerce, offline stores provide convenient and new purchasing experiences to customers by providing product purchasing services through artificial intelligence speakers and personalization service. Although chatbot service is creating business value in some business cases, it is still limited to the process of a specific company, and the spread rate is still slowing because the service scope, convenience, and usefulness are not greater than expected. Therefore, some chatbot development service providers is providing an integrated development platform to improve usability, Chatbots have the features and advantages of providing convenience instead of answering human questions. However, there is a disadvantage that the level of communication can be lowered by reducing various human subjective views and giving mainly objective answers. Through this study, we will discuss the characteristics, advantages and disadvantages of chatbot services by comparing them.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.41-55
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• 2021

AI voice speaker has played an important role in forming an early market and development for AI-based goods and service with growing attention from many people. In this context, this research examined factors affecting continuous intention of AI voice speaker based on the integrated adoption model, which combined two factors of perceived playfulness and innovation resistance with extended technology acceptance model. It was also examined whether three perceived anthropomorphic features(i.e., perceived rational support, perceived intimacy, perceived cognitive openness) have influences on continuous intention of AI voice speaker. The data was collected by an online-survey and were responses of those who are in their 20s and 30s and have experienced in using AI voice speaker. They were analyzed by using SEM(Structural Equation Modeling). The results showed that all of perceived ease of use, perceived usefulness, perceived playfulness and innovation resistance had significant influences on continuous intention of AI voice speaker. In addition, all of perceived rational support, perceived intimacy and perceived cognitive openness as perceived anthropomorphic features had significant influences on perceived ease of use, perceived usefulness and perceived playfulness. The implications of found results in this research was also discussed.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Review of KIISC
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• v.33 no.4
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• pp.117-124
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• 2023

최근 인공지능, 디지털 트윈, 메타버스 기반의 신규 ICT 서비스가 도입되면서 이러한 서비스에서 발생할 수 있는 프라이버시 위험의 적절한 처리는 매우 중요하게 대두되었다. 이의 대표적인 기술적 대책이 프라이버시 강화 기술의 적용이다. 이와 관련하여 개인정보보호 국제표준은 국가나 조직의 관행과 기술을 국제 표준으로 개발하여 상호 연동이 가능한 서비스를 제공하는 역할을 하며, 이로 인해 제품이나 서비스의 경쟁력을 강화하는데 활용될 수 있다. 개인정보보호 국제 표준화를 주도적으로 추진하고 있는 대표적인 국제 표준화 그룹으로는 국제표준화위원회/전기위원회 합동위원회 1/서브위원회 27/작업그룹 5 (ISO/IEC JTC 1/SC 27/WG 5)가 있으며, 독일 쾨테대학의 Kai Rannenberg 교수가 이 그룹의 의장을 맡고 있다. 2022년 ISO/IEC JTC 1/SC 27/WG 5 전자회의 이후 우리나라 주도의 국제표준은 2건 채택되었다. 차기 회의는 2023년 10월 서울에서 원격 참여가 허용된 대면회의로 개최될 예정이다. 본 고에서는 이 그룹에서 2022년 7월 이전 채택된 개인정보보호 관련 국제표준과 현재 개발 중인 주요 국제표준 동향을 살펴보고자 한다. 또한 지난 4월 SC27 WG5 회의에서 논의된 개인정보보호 관련 주요 표준화 이슈와 대응 방안을 제시한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.89-92
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• 2021

In a manufacturing fields, an abnormality or breakdown of equipment is a factor that causes product defects. Recently, with the spread of smart factory services, a lot of research to predict and prevent machine's failures is actively ongoing. However, there is a big difficulty in developing a classification model because the number of abnormal or failure data of the machine is severely smaller than normal data. In this paper, we present an algorithm for detecting abnormalities in an MCT at manufacturing work site depending on the differences between inputs and outputs of Autoencoder model and analyze its performance. The algorithm detects abnormalities using only features of normal data from manufacturing data of the MCT in which abnormal data does not exist.