• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.77-85
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• 2022

Unlike previous studies that focused on the diversity of YouTube content, this study tried to identify factors affecting users' satisfaction with the YouTube recommendation system. Specifically, by adding content preference suitability and privacy concerns to the technology acceptance model, we empirically analyzed how these variables affect user's satisfaction of the YouTube AI recommendation system. For this purpose, asurvey was conducted on college students in their 20s and 30s, and the main research results are as follows. First, in the respondents of this study, playfulness and usefulness, which are major variables of the technology acceptance model, appeared as significant factors affecting the satisfaction of the YouTube AI recommendation system, whereas the effect of ease to use was not found. Second, content preference suitability was found to affect the satisfaction with AI recommendation system, but privacy concerns did not affect the satisfaction with YouTube AI recommendation system. Based on these research results, the implications of the study and the directions for future studies were suggested.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.253-261
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• 2024

The role of the library is as a public institution that provides academic information to a variety of people, including students, the general public, and researchers. These days, as the importance of lifelong education is emphasized, libraries are evolving beyond simply storing and lending materials to complex cultural spaces that share knowledge and information through various educational programs and cultural events. One of the problems library user's faces is locating books to borrow. This problem occurs because of errors in the location of borrowed books due to delays in updating library databases related to borrowed books, incorrect labeling, and books temporarily located in different locations. The biggest problem is that it takes a long time for users to search for the books they want to borrow. In this paper, we propose a system that visually displays the location of books in real time using an AI vision sensor and LED. The AI vision sensor-based book location guidance system generates a QR code containing the call number of the borrowed book. When the AI vision sensor recognizes this QR code, the exact location of the book is visually displayed through LED to guide users to find it easily. We believe that the AI vision sensor-based book location guidance system dramatically improves book search and management efficiency, and this technology is expected to have great potential for use not only in libraries and bookstores but also in a variety of other fields.

• Journal of Aerospace System Engineering
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• v.17 no.2
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• pp.56-61
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• 2023

Recently, advancement of AI-enabled autonomous MUM-T combat system and industrial revitalization are rapidly emerging as global issues. However, the Defense Business Act of the Ministry of National Defense in Korea is judged to be somewhat insufficient compared to NATO leading countries in advancement of operation part of a weapon system as MUM-T is centered on a weapon system's own device. We established the concept of AI-enabled autonomous MUM-T to strengthen international competitiveness of complex combat systems such as future global UGV, UAV, and UMS. In addition, NATO and US-centered autonomy, interoperability, and data standardization-based defense AI MUM-T top-level platform construction and operation plan, establishment of a national defense innovation committee such as the National Science and Technology Advisory Council, review and advisory function reinforcement, and additional governance measures are proposed.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.729-735
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• 2021

Recently Artificial Intelligence(AI) has been developed and used in various fields. Especially AI recognition technology can perceive and distinguish images so it should plays a significant role in quality inspection process. For stability of autonomous driving technology, semiconductors inside automobiles must be protected from external electromagnetic wave(EM wave). As a shield film, a thin polymeric material with hole shaped micro-patterns created by a laser processing could be used for the protection. The shielding efficiency of the film can be increased by the hole structure with appropriate pitch and size. However, since the sensitivity of micro-machining for some parameters, the shape of every single hole can not be same, even it is possible to make defective patterns during process. And it is absolutely time consuming way to inspect all patterns by just using optical microscope. In this paper, we introduce a AI inspection system which is based on web site AI tool. And we evaluate the usefulness of AI model by calculate Area Under ROC curve(Receiver Operating Characteristics). The AI system can classify the micro-patterns into normal or abnormal ones displaying the text of the result on real-time images and save them as image files respectively. Furthermore, pressing the running button, the Hardware of robot arm with two Arduino motors move the film on the optical microscopy stage in order for raster scanning. So this AI system can inspect the entire micro-patterns of a film automatically. If our system could collect much more identified data, it is believed that this system should be a more precise and accurate process for the efficiency of the AI inspection. Also this one could be applied to image-based inspection process of other products.

• Journal of Korean Society for Quality Management
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• v.52 no.3
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• pp.557-574
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• 2024

Purpose: This study investigates whether adoption of AI-based systems and technologies improve operational efficiency in healthcare organizations through a systematic review of the literature and real-world examples. Methods: In this study, we divided the AI application cases into care services and administrative functions, then we explored opportunities and challenges in each area. Results: The analysis results indicate that the care service field primarily uses AI-based systems and technologies for quick disease diagnosis and treatment, surgery and disease prediction, and the provision of personalized healthcare services. In the administrative field, AI-based systems and technologies are used to streamline processes and automate tasks for the following functions: patient monitoring through virtual care support systems; automating patient management systems for appointment times, reservations, changes, and no-shows; facilitating patient-medical staff interaction and feedback through interaction support systems; and managing admission and discharge procedures. Conclusion: The results of this study provide valuable insights and significant implications about the application of AI-based systems or technologies for various innovation opportunities in healthcare organizations. As digital transformation accelerates across all industries, these findings provide valuable information to managers of hospitals that are interested in AI adoption, as well as for policymakers involved in the formulation of medical regulations and laws.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.600-602
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• 2021

With the development of the mobile industry and growing interest in artificial intelligence (AI) technology, a lot of research for AI processors which applicable to embedded systems is under study. When implementing AI to embedded systems, the design should be considered the restriction of resource and power consumption. Moreover, it is efficient to include a dedicated hardware accelerator in order to complement the low computational performance of the embedded system. In this paper, we propose an stand-alone embedded AI processor. The proposed AI processor includes a hardware accelerator that is dedicated to the distance-based AI algorithm and a general-purpose MCU that supports flexible programmability for application to various embedded systems. The AI processor was designed with Verilog HDL and verified by implementing on Field Programmable Gate Array (FPGA).

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.

• Journal of Practical Engineering Education
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• v.16 no.5_spc
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• pp.643-651
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• 2024

This study aims to design and implement an AI mock interview system for Computer Science (CS) interview preparation using LLM (Large Language Model) based ChatGPT. The system utilizes AI's natural language processing and speech recognition capabilities to analyze and provide real-time feedback on interview responses, helping users improve their weaknesses during the preparation process. According to a survey, 90% of users reported that the real-time feedback function provided substantial assistance in their interview preparation. Key features include GPT prompt generation and Speech-to-Text functionality, which converts voice data into text. The system received positive evaluations for its response time and feedback accuracy. Future research will explore expanding the range of question types and applying the system to various industries.

• Journal of Technology Innovation
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• v.29 no.4
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• pp.161-196
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• 2021

This study presents a new methodology for developing AI-based products. It identifies the distinctive attributes of AI innovation that are different from existing methods, and presents a product design process and methodology reflecting these attributes. This study emphasizes that AI product development should be oriented toward an ambidexterity approach. This study proposes a design process and specific development method for AI-based products that including steps such as technology push oriented idea generation with morphological approach, market pull oriented consumer requirements analysis, product design refinement, etc. In order to verify the practical applicability of this methodology, an AI-based car infotainment system development strategy is derived as a case study. 13 innovative ideas were generated by the morphological approach and expert review based on technological possibility, and a total of 6 quality requirements were derived as new product development strategies through the analysis of consumer requirements by combining Kano and TOPSIS. The methodology proposed in this research paper can be usefully utilized for companies to pioneer new markets through AI-based products or to expand the market by upgrading existing products or services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2961-2975
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• 2022

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.