• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.81-88
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• 2022

This study is to investigate the perception of domestic appraisers about the possibility of using artificial intelligence (AI) and related risks from the use of AI in the appraisal industry. We conducted a mobile survey of evaluators from February 10 to 18, 2022. We collected survey data from 193 respondents. Frequency analysis and multiple response analysis were performed for basic analysis. When AI is used in the appraisal industry, factor analysis was used to analyze various types of risks. Although appraisers have a positive perception of AI introduction in the appraisal industry, they considered collateral, consulting, and taxation, mainly in areas where AI is likely to be used and replaced, mainly negative effects related to job losses and job replacement. They were more aware of the alternative risks caused by AI in the field of human labor. I was very aware of responsibilities, privacy and security, and the risk of technical errors. However, fairness, transparency, and reliability risks were generally perceived as low risk issues. Existing studies have mainly studied analysis methods that apply AI to mass evaluation models, but this study focused on the use and risk of AI. Understanding industry experts' perceptions of AI utilization will help minimize potential risks when AI is introduced on a large scale.

• Journal of Science Education
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• v.45 no.1
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• pp.105-117
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• 2021

The purpose of this study is to analyze the gender differences of elementary, junior high, and high school students in the artificial intelligence ethics awareness (hereinafter referred to as AIEA). This is a study to investigate whether there is a gender difference in the AIEA, and if so, when the gender difference will occur. This study was conducted with 198 elementary school students (98 female students, 100 male students), 265 middle school students (166 female students, 99 male students), and 114 high school students (58 female students and 56 male students) in I Metropolitan City. The results are as follows: First, a gender difference in the AIEA between all boys and girls was confirmed. Second, the gender difference in the AIEA tended to be solidified as the school age increased from elementary school to middle school and high school. Third, female students at all stages of elementary school, junior high school, and high school are not yet very reliable in artificial intelligence, and there is a greater concern about non-discrimination than boys. It turns out that they have a negative position on permission to enter the territory. Fourth, the interaction effects of school age and gender have been identified in 'stability and reliability,' and in 'permit and limit' categories. Taken together, these results show that an educational strategy that approaches the gender equality perspective of the educational program is necessary so that there will be no gender difference in the AIEA during artificial intelligence education activities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2086-2097
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• 2021

The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.85-92
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• 2022

Demand for big data analysis and AI developers is increasing, but there is a lack of an education base to supply them. In this paper, by developing a cloud-based artificial intelligence education platform, the goal was to establish an environment in which practical practical training can be efficiently learned at low cost at educational institutions and IT companies. The development of the education platform was carried out by planning scenarios for each user, architecture design, screen design, implementation of development functions, and hardware construction. This training platform consists of a containerized workload, service management platform, lecture and development platform for instructors and students, and secured cloud stability through real-time alarm system and age test, CI/CD development environment, and reliability through docker image distribution. The development of this education platform is expected to expand opportunities to enter new businesses in the education field and contribute to fostering working-level human resources in the AI and big data fields.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.71-86
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• 2022

The purpose of this study is to develop and validate a scale to measure the ethical awareness of users who accept artificial intelligence technology or service. To this end, the constructs and properties of AI ethics were identified through literature analysis on AI ethics. Reliability and validity were assessed through a preliminary survey(N=273), after conducting an open-type survey to men and women(N=133) in 10s to 70s nationwide, extracting the first questions, and reviewing them by experts. The results of an online survey conducted on men and women(N=500) were refined by confirmatory factor analysis. Finally, an AI technology ethics scale was developed. The AI technology ethics awareness scale was developed with 16 questions in total of 4 factors (transparency, safety, fairness, accountability) so that general awareness of ethics related to AI technology can be measured by detailed factors. In addition, through follow-up research, it will be possible to reveal the relationship with measurement variables in various fields by using the ethical awareness scale of artificial intelligence technology.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.873-879
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• 2023

This study explored the use of artificial intelligence(AI) to detect foreign bodies in chest X-ray images. Medical imaging, especially chest X-rays, plays a crucial role in diagnosing diseases such as pneumonia and lung cancer. With the increase in imaging tests, AI has become an important tool for efficient and fast diagnosis. However, images can contain foreign objects, including everyday jewelry like buttons and bra wires, which can interfere with accurate readings. In this study, we developed an AI algorithm that accurately identifies these foreign objects and processed the National Institutes of Health chest X-ray dataset based on the YOLOv8 model. The results showed high detection performance with accuracy, precision, recall, and F1-score all close to 0.91. Despite the excellent performance of AI, the study solved the problem that foreign objects in the image can distort the reading results, emphasizing the innovative role of AI in radiology and its reliability based on accuracy, which is essential for clinical implementation.

• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.101-140
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• 2021

As the payment service sector actively embraces artificial intelligence technology, "Voice Payments" is becoming a trend in contactless payment services. Voice payment services can execute payments faster and more intuitively through "voice," the most natural means of communication for humans. In this study, we selected richness, intimacy, and autonomy as factors for building trust with artificial intelligence agents. We wanted to determine whether the trust will be formed if the factors were applied to the voice payment services. The experiment results showed that the higher the richness and autonomy of the voice payment interface and the lower the intimacy, the higher the trust. In addition, the two-way interaction effects of richness and autonomy were significant. We analyzed and synthesized the collected short-answer system to identify users' anxiety when using voice payment services and proposed speech interface design ideas to increase their trust in the voice payment.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.259-270
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• 2022

Artificial intelligence (AI) has been studied in various fields of medical imaging. Currently, top-notch deep learning (DL) techniques have led to high diagnostic accuracy and fast computation. However, they are rarely used in real clinical practices because of a lack of reliability concerning their results. Most DL models can achieve high performance by extracting features from large volumes of data. However, increasing model complexity and nonlinearity turn such models into black boxes that are seldom accessible, interpretable, and transparent. As a result, scientific interest in the field of explainable artificial intelligence (XAI) is gradually emerging. This study aims to review diverse XAI approaches currently exploited in medical imaging. We identify the concepts of the methods, introduce studies applying them to imaging modalities such as computational tomography (CT), magnetic resonance imaging (MRI), and endoscopy, and lastly discuss limitations and challenges faced by XAI for future studies.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.23-30
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• 2024

This paper studies a GPS error filtering method that takes into account the continuity of the ongoing path to enhance the safety of autonomous agricultural mobile robots. Real-Time Kinematic Global Positioning System (RTK-GPS) is increasingly utilized for robot position evaluation in outdoor environments due to its significantly higher reliability compared to conventional GPS systems. However, in orchard environments, the robot's current position obtained from RTK-GPS information can become unstable due to unknown disturbances like orchard canopies. This problem can potentially lead to navigation errors and path deviations during the robot's movement. These issues can be resolved by filtering out GPS information that deviates from the continuity of the waypoints traversed, based on the robot's assessment of its current path. The contributions of this paper is as follows. 1) The method based on the previous waypoints of the traveled path to determine the current position and trajectory. 2) GPS filtering method based on deviations from the determined path. 3) Finally, verification of the navigation errors between the method applying the error filter and the method not applying the error filter.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.510-518
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• 2022

This paper confirmed the technical reliability of mobile-based sarcopenia prediction and monitoring system. In implementing the developed system, we designed using only sensors built into a smartphone without a separate external device. The prediction system predicts the possibility of sarcopenia without visiting a hospital by performing the SARC-F survey, the 5-time chair stand test, and the rapid tapping test. The Monitoring system tracks and analyzes the average walking speed in daily life to quickly detect the risk of sarcopenia. Through this, it is possible to rapid detection of undiagnosed risk of undiagnosed sarcopenia and initiate appropriate medical treatment. Through prediction and monitoring system, the user may predict and manage sarcopenia, and the developed system can have a positive effect on reducing medical demand and reducing medical costs. In addition, collected data is useful for the patient-doctor communication. Furthermore, the collected data can be used for learning data of artificial intelligence, contributing to medical artificial intelligence and e-health industry.