• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.815-820
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• 2023

In this paper, the design method of a miniaturized chipless RFID tag using a modified bent H-shaped slot was proposed. The proposed modified bent H-shaped slot was appended on the rectangular conductor plate printed on one side of a 20 mm × 50 mm FR4 substrate with a thickness of 0.8 mm. The resonant dip frequency of the bistatic RCS for the proposed modified bent H-shaped slot was compared with the cases when the H-shaped, U-shaped slot, and bent H-shaped slots were added, respectively, on the conductor plate. The simulated resonant dip frequencies for H-shaped, U-shaped, and bent H-shaped slots were 5.907 GHz, 4.918 GHz, and 4.364 GHz, respectively. When the proposed modified bent H-shaped slot was added, the resonant dip frequency was decreased to 3.741 GHz, and, therefore, the slot length was reduced by 36.7% compared to the H-shaped slot case. Experiment results show that the resonant dip frequency of the fabricated modified bent H-shaped slot was 3.9 GHz.

• Information Systems Review
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• v.25 no.1
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• pp.47-73
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• 2023

Management decision-making based on artificial intelligence(AI) plays an important role in helping decision-makers. Business decision-making centered on AI is evaluated as a driving force for corporate growth. AI-based on accurate analysis techniques could support decision-makers in making high-quality decisions. This study proposes an effective decision-making method with the application of multi-label feature selection. In this regard, We present a CFS-BR (Correlation-based Feature Selection based on Binary Relevance approach) that reduces data sets in high-dimensional space. As a result of analyzing sample data and empirical data, CFS-BR can support efficient decision-making by selecting the best combination of meaningful attributes based on the Best-First algorithm. In addition, compared to the previous multi-label feature selection method, CFS-BR is useful for increasing the effectiveness of decision-making, as its accuracy is higher.

• Korea Journal of Hospital Management
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• v.28 no.4
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• pp.73-89
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• 2023

Purposes: The purpose of this study was to empirically analyze the effect of the attitude of medical staff providing medical services on the treatment satisfaction of the patients who experienced outpatient care at the hospitals and clinics. In particular, it was verified whether the courtesy of the medical staff to the outpatients has moderated the effect of the medical staff's explanation on the treatment satisfaction. Methodology: After controlling the socio-demographic factors of the outpatients with their treatment and waiting time, multiple regression analyses were conducted to figure out the effect of the attitude of the medical staff on the treatment satisfaction. And the covariance analyses were adopted to verify the moderating effect of the variables of the medical staff. Findings: At both hospitals and clinics, all attitudes of medical staff such as the way they explain to and communicate with the patients, and their courtesy showed positive effects on treatment satisfaction. Among them, the courtesy of the medical staff was the most influential variable on the satisfaction of the treatment, and it only had the control power over the effect of the way they explain on the treatment satisfaction. Practical Implication: Among the medical staff's attitudes toward patients at hospital or clinic level, the courtesy of doctors and nurses is an important factor in improving treatment satisfaction. In particular, if the level of their courtesy is low among the medical services rendered at the clinics, the satisfaction level will decrease even if the level of explanation of the medical staff is high. Therefore, in terms of hospital management, treatment satisfaction can be improved when doctors and nurses provide medical services to visitors with polite, humble and friendly manner in explaining to and communicating with the patients.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.309-316
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• 2024

Facial recognition technology is widely used in various fields but faces challenges due to its vulnerability to fraudulent activities such as photo spoofing. Extensive research has been conducted to overcome this challenge. Most of them, however, require the use of specialized equipment like multi-modal cameras or operation in high-performance environments. In this paper, we introduce LH-FAS v2 (: Lightweight Head-pose-based Face Anti-Spoofing v2), a system designed to operate on a commercial webcam without any specialized equipment, to address the issue of facial recognition spoofing. LH-FAS v2 utilizes FSA-Net for head pose estimation and ArcFace for facial recognition, effectively assessing changes in head pose and verifying facial identity. We developed the VD4PS dataset, incorporating photo spoofing scenarios to evaluate the model's performance. The experimental results show the model's balanced accuracy and speed, indicating that head pose estimation-based facial anti-spoofing technology can be effectively used to counteract photo spoofing.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.301-307
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• 2024

This study introduces a novel cascade fusion architecture aimed at enhancing thermal images across various scale conditions. The processing of thermal images at multiple scales has been challenging due to the limitations of existing methods that are designed for specific scales. To overcome these limitations, this paper proposes a unified framework that utilizes cascade feature fusion to effectively learn multi-scale representations. Confidence maps from different image scales are fused in a cascaded manner, enabling scale-invariant learning. The architecture comprises end-to-end trained convolutional neural networks to enhance image quality by reinforcing mutual scale dependencies. Experimental results indicate that the proposed technique outperforms existing methods in multi-scale thermal image enhancement. Performance evaluation results are provided, demonstrating consistent improvements in image quality metrics. The cascade fusion design facilitates robust generalization across scales and efficient learning of cross-scale representations.

• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.3-27
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• 2022

Cardiac CT has been proven to provide diagnostic and prognostic evaluation of coronary artery disease for cardiovascular risk stratification and treatment decision-making based on rapid technological development and various research evidence. Coronary CT angiography has emerged as a gateway test for coronary artery disease that can reduce invasive angiography due to its high negative predictive value, but the diagnostic specificity is relatively low. However, coronary CT angiography is likely to overcome its limitations through functional evaluation to identify the hemodynamic significance of coronary artery disease by analyzing myocardial perfusion and fractional flow reserve through cardiac CT. Recently, studies have been actively conducted to incorporate artificial intelligence to make this more objective and reproducible. In this review, functional imaging techniques of cardiac computerized tomography are explored.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.539-544
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• 2024

This paper examines the necessity and direction of digital literacy education as university education in the AI era. Digital literacy can be considered universal education about everyday culture in a digital environment, and its scope is expanding to cultivate the competencies necessary for citizens of a digital society, rather than simply the ability to use digital devices. In this paper, the university liberal arts curriculum has strengthened the information literacy area to reflect the changes of the times, but it is presented as a problem that it is still focused on the technical aspects of learning how to use digital devices and specific programs. It was suggested that the direction of digital literacy education in universities should not be limited to the technical and instrumental aspects of using digital devices, but that it would be desirable to focus on digital ethics considering the social impacts that may arise from the use of digital devices.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.625-632
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• 2024

This paper analyzed the changes in the curriculum of computer and software-related majors that educate the core ICT technologies needed for the 4th Industrial Revolution, before and after the COVID-19 pandemic. According to the standard classification of university education units, 172 majors classified into Applied Software Engineering, Computer Science·Computer Engineering, and Artificial Intelligence Engineering were targeted, and the curricula of 2023 and 2019 were compared and analyzed. As a result of the analysis, the introduction of the related curriculum for each curriculum group increased by about 2.6%p before and after the COVID-19 pandemic (2023 84.2%, 2019 81.6%). and the 4th Industrial Revolution response index increased by 9.5 points (37.0 in 2023, 27.5 in 2019)

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.515-522
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• 2024

Energy consumption is steadily increasing, and buildings in particular account for more than 20% of the total energy consumption around the world. As an effort to cost-effectively manage the energy consumption of buildings, many research groups have recently focused on Smart Building Energy Management Systems (BEMS), which are deepening the research depth by applying artificial intelligence(AI). In this paper, we propose a reinforcement learning-based energy control method for smart energy buildings integrated with V2G station, which aims to reduce the total energy cost of the building. The results of performance evaluation based on the energy consumption data measured in the real-world building shows that the proposed method can gradually reduce the total energy costs of the building as the learning process progresses.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1290-1304
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• 2020

Medical image analyses have been widely used to differentiate normal and abnormal cases, detect lesions, segment organs, etc. Recently, owing to many breakthroughs in artificial intelligence techniques, medical image analyses based on deep learning have been actively studied. However, sufficient medical data are difficult to obtain, and data imbalance between classes hinder the improvement of deep learning performance. To resolve these issues, various studies have been performed, and data augmentation has been found to be a solution. In this review, we introduce data augmentation techniques, including image processing, such as rotation, shift, and intensity variation methods, generative adversarial network-based method, and image property mixing methods. Subsequently, we examine various deep learning studies based on data augmentation techniques. Finally, we discuss the necessity and future directions of data augmentation.