• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.348-350
• /
• 2021

The core technology of augmented reality is object recognition technology. Recently, due to the development of various artificial intelligence algorithms such as CNN, it has become possible to effectively distinguish specific objects from images. It is possible to realize more realistic and immersive augmented reality contents only when technology for recognizing objects quickly and accurately is secured. In this study, an object recognition model using SSD (single shot multibox detector) and an object recognition model using YOLO were compared and evaluated.

• Journal of Oral Medicine and Pain
• /
• v.47 no.2
• /
• pp.107-108
• /
• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.3
• /
• pp.93-98
• /
• 2011

In this paper, we propose a game type cognitive rehabilitation system using marker-based augmented reality system for intelligence development of user. Existing cognitive rehabilitation with the help of others, or a keyboard or mouse operation required to relieve the discomfort, the marker card only control it led and is advanced the method which it applied. As a result, obtained through the camera calibration for image processing, and a Augmented Reality as well as mark detection. In this paper we presented a complete rotation of the model after checking through the whole form, through a combination of multiple markers by completing the interactive objects proceed with the rehabilitation process in a manner required by the target of interest to human rehabilitation and treatment.

• Convergence Security Journal
• /
• v.20 no.2
• /
• pp.91-99
• /
• 2020

The purpose of this study is to present the application of Information and Communication Technology(ICT) during the 4th Industrial Revolution for the efficient implementation of government emergency preparedness policies. Brainstorming by experts categorized the government's emergency preparedness policies into 4 types and 12 detailed tasks. Classification results were used by AHP(Analytic Hierarchy Process) to analyze relative importance and priorities. The AHP survey found that strengthening crisis management responsiveness was the most important detailed task. Artificial Intelligence(AI), Internet of Things(IoT), Unmanned Autonomy System, Virtual Reality(VR), and Augmented Reality(AR) were presented as major information and communication technology(ICT) for the efficient execution of detailed tasks.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.231-252
• /
• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of Digital Convergence
• /
• v.17 no.7
• /
• pp.311-316
• /
• 2019

The mobile game environment is rapidly expanding with AR (augmented reality) technology along with artificial intelligence. In particular, AR (Augmented Reality) technology is a field of VR (Virtual Reality), which is a technology that shows a mixture of virtual information and images in a real environment. Recently, research on mobile UI design based on the interaction based on the augmented reality technology has become important at the point when various utilization methods are suggested based on understanding of contents. There are still some issues in terms of whether the consumer can utilize it in various ways, unlike the developed supply system. In this paper, we present an example of mobile UI design based on interaction based on smart media augmented reality through previous study and literature study of smart augmented reality to solve problem UI issues based on background theory.

• Journal of Multimedia Information System
• /
• v.7 no.1
• /
• pp.11-16
• /
• 2020

Deep Learning has become the most important technology in the field of artificial intelligence machine learning, with its high performance overwhelming existing methods in various applications. In this paper, an interactive window service based on object recognition technology is proposed. The main goal is to implement an object recognition technology using this deep learning technology to remove the existing eye tracking technology, which requires users to wear eye tracking devices themselves, and to implement an eye tracking technology that uses only usual cameras to track users' eye. We design an interactive system based on efficient eye detection and pupil tracking method that can verify the user's eye movement. To estimate the view-direction of user's eye, we initialize to make the reference (origin) coordinate. Then the view direction is estimated from the extracted eye pupils from the origin coordinate. Also, we propose a blink detection technique based on the eye apply ratio (EAR). With the extracted view direction and eye action, we provide some augmented information of interest without the existing complex and expensive eye-tracking systems with various service topics and situations. For verification, the user guiding service is implemented as a proto-type model with the school map to inform the location information of the desired location or building.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.426-431
• /
• 1998

We propose a fuzzy rule based method for structure preserving dimensionality reduction. This method selects a small representative sample and applies Sammon's method to project it. The input data points are then augmented by the corresponding projected(output) data points. The augmented data set thus obtained is clustered with the fuzzy c-means(FCM) clustering algorithm. Each cluster is then translated into a fuzzy rule for projection. Our rule based system is computationally very efficient compared to Sammon's method and is quite effective to project new points, i.e., it has good predictability.

• Electronics and Telecommunications Trends
• /
• v.32 no.6
• /
• pp.66-72
• /
• 2017

The frequency range of a radio wave is from 3kHz to 300GHz, and radio technologies use this range to improve the quality of human lives. Radio technologies have entered a new phase of communication. The core infrastructure used as the basis for technologies leading the fourth industrial evolution, such as artificial intelligence, the Internet of Things, autonomous cars/drones, augmented reality, robots, and remote medical diagnoses, is the 5G network. The 5G network enables transmitting and receiving large amounts of data at very high speed. In particular, application technologies with artificial intelligence have been studied, including radar, wireless charging, electromagnetic devices and their effects on humans, EMI/EMC, and microwave imaging. In this study, we present a future radio technology that is needed to prepare for the upcoming industrial revolution and digital transformation.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.34 no.1
• /
• pp.71-82
• /
• 2024

With the advancement of information and communication technology (ICT), the use of electronic document types such as PDF, MS Office, and HWP files has increased. Such trend has led the cyber attackers increasingly try to spread malicious documents through e-mails and messengers. To counter such attacks, AI-based methodologies have been actively employed in order to detect malicious document files. The main challenge in detecting malicious HWP(Hangul Word Processor) files is the lack of quality dataset due to its usage is limited in Korea, compared to PDF and MS-Office files that are highly being utilized worldwide. To address this limitation, data augmentation have been proposed to diversify training data by transforming existing dataset, but as the usefulness of the augmented data is not evaluated, augmented data could end up harming model's performance. In this paper, we propose an effective semi-supervised learning technique in detecting malicious HWP document files, which improves overall AI model performance via quantifying the utility of augmented data and filtering out useless training data.